注：机翻，未校。
Scientific Method
First published Fri Nov 13, 2015; substantive revision Tue Jun 1, 2021
Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).
科学是一项非常成功的人类事业。对科学方法的研究是试图辨别实现这种成功的活动。通常被认为是科学特征的活动包括系统观察和实验、归纳和演绎推理以及假设和理论的形成和测试。这些具体执行方式可能大相径庭，但像这样的特征被视为区分科学活动和非科学活动的一种方式，只有采用某种规范形式的科学方法或方法的企业才应被视为科学。其他人质疑是否有像固定的方法工具包这样的东西，这在科学中是通用的，而且只有科学是通用的。一些人拒绝将一种方法视为拒绝关于科学本质的更广泛观点的一部分，例如自然主义（Dupré 2004）;有些人原则上拒绝任何限制（多元主义）。
Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.
科学方法应与科学的目标和产品区分开来，例如知识、预测或控制。方法是实现这些目标的手段。科学方法也应与元方法区分开来，元方法包括科学方法（即方法）的特定特征背后的价值和理由——诸如客观性、可重复性、简单性或过去的成功等价值。方法论规则被提出来管理方法，遵守这些规则的方法是否满足给定的值是一个元方法论问题。最后，在某种程度上，方法与实施方法的详细和上下文实践不同。后者可能包括：特定的实验室技术;用于描述和推理的数学形式主义或其他专业语言;技术或其他物质手段;与其他科学家或广大公众交流和分享结果的方式;或者约定俗成、习惯、强制习俗和对科学实施方式和内容的制度控制。
While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.
虽然认识到这些区别很重要，但它们的界限是模糊的。因此，对方法的描述不能完全脱离其方法论和元方法论的动机或理由，此外，每个方面在识别方法方面都起着至关重要的作用。因此，关于方法的争议在细节、规则和元规则层面上演。例如，对科学知识的确定性或易错性的看法的变化（这是对我们可以希望方法提供什么的元方法论考虑）意味着对演绎和归纳推理的不同强调，或者对推理与观察的相对重要性（即特定方法的差异）的不同强调。关于科学在社会中的作用的信念将影响一个人在科学方法中对价值观的重视。
The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.
在过去的半个世纪里，对科学方法的辩论影响最大的问题是，我们需要对方法有多多的多元化？统一论者继续坚持一种对科学至关重要的方法;虚无主义是激进多元主义的一种形式，它认为任何方法论处方的有效性都非常敏感，以至于它本身无法解释。关于科学实践中体现的方法，一些中等程度的多元化似乎是合适的。但科学实践的细节会因时间和地点、机构、科学家及其研究对象而异。这些变化对理解科学及其成功有多重要？方法可以从实践中抽象出多少？本条目描述了一些描述科学方法或方法的尝试，以及对嵌入实际科学实践中的方法采取更上下文敏感方法的论点。
1. Overview and organizing themes 1. 概述和组织主题
This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.
这个条目本可以命名为“科学方法”，并深入探讨，足以写成多卷书籍；或者，它可以极其简短，仅提供一个简短的概述，拒绝了存在唯一科学方法的想法。这两种不尽如人意的可能性都是由于科学活动在不同学科、不同时间、不同地点以及不同科学家之间存在着极大的差异，以至于任何试图全面概括的描述，要么会包含过多的描述性细节，要么则会沦为无关紧要的笼统概括。
The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.
本文的范围选择更加乐观，它从最近的科学哲学运动中得到了启发，更加关注实践：科学家的实际工作。这种“转向实践”可以被视为对科学方法的最新研究形式，因为它代表了理解科学活动的尝试，但通过既不是普遍和统一的，也不是单一和狭隘描述的叙述。在某种程度上，不同的科学家在不同的时间和地点可以说使用相同的方法，尽管在实践中，细节是不同的。
Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.
实施方法的背景是否相关，或在多大程度上，将在很大程度上取决于一个人认为科学的目标是什么以及他自己的目标是什么。在科学方法论的大部分历史中，假设科学最重要的产出是知识，因此方法论的目标应该是发现产生科学知识的那些方法。
Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.
科学被视为最成功的推理形式（但哪种形式？）在系统收集的证据的基础上（但究竟是哪种形式？）最确定的知识主张（但有多确定呢？）什么才算是证据，感官的证据应该优先，还是理性的洞察力？第 2 节调查了一些历史，指出了两个主要主题。一个主题是在观察和推理（以及随之而来的采用它们的推理形式）之间寻求适当的平衡;另一个是某些科学知识如何或可以如何。
Section 3 turns to 20th century debates on scientific method. In the second half of the 20th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.
第 3 节转向 20 世纪关于科学方法的辩论。在 20 世纪下半叶，科学的认识特权面临一些挑战，许多科学哲学家放弃了对科学方法逻辑的重建。关于应该捕捉科学的哪些功能以及为什么，人们的看法发生了重大变化。对一些人来说，科学的成功更能与社会或文化特征相联系。科学哲学的历史和社会学转向，要求更多地关注科学的非认识方面，例如社会学、制度、物质和政治因素。即使在这些运动之外，科学哲学的专业化也越来越严重，越来越关注科学中的特定领域。综合起来的结果是，很少有哲学家不再争论一个宏伟的统一科学方法论。第 3 节和第 4 节调查了 20 世纪科学哲学中关于科学方法的主要立场，重点关注它们在偏好确认或证伪或完全放弃特殊科学方法的想法方面存在差异。
In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.
近几十年来，人们主要关注传统上属于方法范畴的科学活动，例如实验设计和一般实验室实践、统计数据的使用、模型和图表的构建和使用、跨学科合作和科学传播。第 4-6 节试图构建当前科学方法研究领域的地图。
As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.
正如这些章节所说明的，方法问题仍然是关于科学的论述的核心。科学方法仍然是教育、科学政策和科学家的话题。它出现在公共领域，科学的界限或地位存在争议。因此，一些哲学家最近又回到了这个问题上，即是什么使科学成为一种独特的文化产品。本文将以最近辨别和概括获得科学知识的活动的一些尝试作为结束。
2. Historical Review: Aristotle to Mill 2. 历史回顾：从亚里士多德到密尔
Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences.[1]
尝试科学方法的历史使该主题的广阔范围复杂化。本节简要调查了现代方法论辩论的背景。可以称为古典观点的东西可以追溯到古代，代表了后来分歧的起点。[1]
We begin with a point made by Laudan (1968) in his historical survey of scientific method:
我们从 Laudan （1968） 在他对科学方法的历史调查中提出的观点开始：
Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)
也许科学方法理论史作为一个受人尊敬的研究领域出现的最严重阻碍是倾向于将其与认识论通史混为一谈，从而假设适用于后者的叙述类别和分类鸽子洞也是前者的基础。(1968: 5)
To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science).
将关于自然世界的知识更普遍地归入知识范畴，是一种可以理解的混淆。方法理论的历史自然会采用相同的叙述类别和分类鸽子洞。例如，认识论历史的一个重要主题是知识的统一，这个主题反映在科学方法的统一问题上。那些确定了各种知识差异的人，往往也同样确定了获得这种知识的不同方法。
Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible (The Republic, 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature (Metaphysics Z, in Barnes 1984).
关于什么是已知的、如何已知的以及什么是可以知道的不同观点是相互关联的。柏拉图将事物的领域分为可见的和可理解的（The Republic， 510a， in Cooper 1997）。只有后者，即形式，才能成为知识的对象。可理解的真理可以通过几何学和演绎推理的确定性来了解。然而，从定义上讲，物质世界所能观察到的并不完美和欺骗性，并不理想。因此，柏拉图式的知识方式强调推理作为一种方法，淡化了观察的重要性。亚里士多德不同意，他将自然界的形式定位为通过对自然的探究来发现的基本原则（形而上学 Z，在 Barnes 1984 中）。
Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics, Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science (epistêmê) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality).
亚里士多德被公认为是西方传统中最早的系统论述科学探究本质的论文，该论著包括对自然界的观察和推理。在《先验分析》和《后验分析》中，亚里士多德首先反思了对自然的探究目标，然后是研究自然的方法。可以找到许多特征，这些特征仍然被大多数人认为对科学至关重要。对亚里士多德来说，经验主义，仔细观察（但被动观察，不是对照实验）是起点。不过，目的不仅仅是记录事实。对亚里士多德来说，科学 （epistêmê） 是一系列经过适当安排的知识或学习——经验事实以及它们的排序和展示都至关重要。发现、排序和展示事实的目标在一定程度上决定了成功的科学探究所需的方法。同样决定性的是所寻求的知识的性质，以及这种知识所特有的解释性原因。
In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon. This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/synthesis, non-ampliative/ampliative, or even confirmation/verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.
因此，除了仔细观察之外，科学方法还需要一种逻辑作为推理系统，以便正确安排并通过观察了解的内容，但也要推断出之外的东西。推理方法可能包括归纳、预测或类比等。亚里士多德的系统（连同他的谬误推理目录）被收集在 Organon 的标题下。这个标题在后来的科学推理著作中得到了呼应，例如弗朗西斯·培根 （Francis Bacon） 的 Novum Organon 和威廉·惠威尔 （William Whewell） 的 Novum Organon Restorum（见下文）。在亚里士多德的 Organon 中，推理主要分为两种形式，一种粗略的划分一直持续到现代。这种划分，今天最常见的是演绎法与归纳法，在其他时代和方法中以分析/综合、非放大/放大，甚至确认/验证的形式出现。基本思想是，在我们的探究方法中有两个“方向”：一个从观察到的内容，到更基本、更普遍和更全面的原则;另一个，从基本和一般到原则的实例或含义。
The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application.[2] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.
这里确定的基本目的和探究方法可以被视为贯穿未来两千年对寻求知识的正确方式的反思的主题：仔细观察自然，然后寻求解释或预测其运作的规则或原则。亚里士多德语料库为独立于科学本身（宇宙与物理）的科学方法的评论传统提供了框架。在中世纪时期，阿尔伯图斯·马格努斯（Albertus Magnus，1206-1280 年）、托马斯·阿奎那（Thomas Aquinas，1225-1274 年）、罗伯特·格罗斯泰斯特（Robert Grosseteste，1175-1253 年）、罗杰·培根（Roger Bacon，1214/1220-1292 年）、奥卡姆的威廉（William of Ockham，1287-1347 年）、安德烈亚斯·维萨里乌斯（Andreas Vesalius，1514-1546 年）、贾科莫·扎巴雷拉（Giacomo Zabarella，1533-1589 年）等人物都致力于阐明通过观察和归纳获得的知识类型、归纳的合理来源以及应用归纳的最佳规则。[2] 我们现在认为他们的许多贡献对科学至关重要（另见 Laudan 1968）。正如亚里士多德和柏拉图采用了“对形式”或“远离形式”的推理框架，中世纪的思想家采用了远离现象或回到现象的方向。在分析中，对现象进行了检查以发现其基本的解释原则;在综合中，对现象的解释是根据第一原理构建的。
During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16th–18th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists; Boyle; Henry More; Galileo).
在科学革命期间，这些不同的论证、实验和理性被锻造成占主导地位的认识权威。16 至 18 世纪不仅是自然界运作知识取得巨大进步的时期——机械、医学、生物、政治、经济解释的进步——而且是对正在发生的革命性变化的自我认识的时期，也是对取得进步的方法的来源和合法性的强烈反思的时期。建立新权威的斗争包括方法论上的改变。根据伽利略·伽利莱（Galileo Galilei，1564-1642 年）或弗朗西斯·培根（Francis Bacon，1561-1626 年）的比喻，《自然之书》是用数学、几何学和数字的语言写成的。这促使强调数学描述和机械解释作为科学方法的重要方面。通过亨利·莫尔 （Henry More） 和拉尔夫·库德沃斯 （Ralph Cudworth） 等人物，新柏拉图主义强调形而上学对表象背后自然的反思的重要性，特别是将精神作为纯粹机械的补充，仍然是科学革命的重要方法论线索。
In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.
在 Novum Organum（1620 年）中，培根批评了亚里士多德的方法，即过快地从特殊性跳到普遍性。推理的三段论形式很容易将这两种类型的命题混合在一起。培根旨在发明新的艺术、原则和方向。他的方法将建立在有条不紊的观察收集的基础上，再加上对我们感官的纠正（特别是避免偶像的方向，正如他所说的，天真的观察者容易出现的系统性错误）。然后，科学家群体可以通过谨慎、渐进和不间断的攀升，攀升到可靠的一般主张。
Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon).
培根的方法被批评为不切实际，对于实践科学家来说太不灵活了。惠威尔后来在他的《逻辑体系》中批评培根对科学家的实践关注太少。在科学史上很难找到培根方法付诸实践的令人信服的例子，但有一些例子被奉为 16 世纪科学归纳法的真实例子，即使不是死板的培根模式：罗伯特·波义耳（Robert Boyle，1627-1691 年）和威廉·哈维（William Harvey，1578-1657 年）等人物。
It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks, this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia.[3] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World (Principia, Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy.)
然而，艾萨克·牛顿（Isaac Newton，1642-1727 年）是科学史学家和方法论者最关注的。鉴于他的 Principia Mathematica 和 Opticks 的巨大成功，这是可以理解的。对牛顿方法的研究有两个主要推力：Opticks 中提出的实验和推理的隐含方法，以及《原理》第三卷中给出的明确方法论规则，即哲学规则（规则）。[3] 牛顿的万有引力定律是他新宇宙学的关键，它打破了自然哲学的解释惯例，首先是显然提出了远距离的行动，但更普遍的是没有提供“真实”的物理原因。他的世界体系（Principia，第三卷）的论点是基于现象，而不是理性的第一性原理。这被认为（主要是在欧洲大陆）不足以构成正确的自然哲学。《古尔谕》反驳了这一反对意见，通过重新定义自然哲学家应该遵循的方法，重新定义了自然哲学的目标。
To his list of methodological prescriptions should be added Newton’s famous phrase “hypotheses non fingo” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton, Leibniz, Descartes, Boyle, Hume, enlightenment, as well as Shank 2008 for a historical overview.)
在他的方法论建议中，应该补充牛顿著名的名言“hypotheses non fingo”（通常翻译为“我不假设”）。科学家不应创造系统，而应根据观察推导解释，正如培根所倡导的。这被称为归纳法。在牛顿之后的一个世纪，牛顿方法的重要澄清相继出现。例如，科林·麦克劳林（1698–1746）重建了该方法的基本结构，将其划分为互补的分析和综合阶段，一个从现象出发进行概括，另一个则从一般命题出发推导新现象的解释。丹尼斯·狄德罗（1713–1784）和《百科全书》的编辑们在巩固和普及牛顿主义方面做出了重要贡献，弗朗切斯科·阿尔加罗蒂（1721–1764）也是其中一位重要人物。这些强调往往不仅在于科学家的过程，也在于科学家的品格，这种品格至今仍被普遍认为。科学家在自然面前谦逊，不受教条束缚，只相信自己的观察，追随真理，无论它将他们引向何方。显然，伏尔泰（1694–1778）和杜·查特莱（1706–1749）在传播这种科学家及其工艺的愿景方面影响最为深远，牛顿被视为英雄。科学方法成为启蒙时代的革命性力量。
Not all 18th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley; David Hume; Hume’s Newtonianism and Anti-Newtonianism). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.
并非所有 18 世纪对科学方法的反思都如此值得庆祝。著名的还有乔治·伯克利（George Berkeley，1685-1753 年）对新科学数学的攻击，以及牛顿学派对观察的过分强调;以及大卫·休谟（David Hume，1711-1776 年）通过归纳论证破坏了为科学主张提供的保证（参见以下条目：乔治·伯克利;大卫·休谟;休谟的牛顿主义和反牛顿主义）。休谟的归纳问题促使伊曼纽尔·康德（Immanuel Kant，1724-1804 年）为实证方法寻找新的基础，尽管作为一种认识论的重建，而不是作为科学家的任何一套实用指南。休谟和康德都影响了下个世纪的方法论思考，例如穆勒和惠威尔之间关于科学中归纳推理确定性的辩论。
The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell.)
约翰·斯图尔特·穆勒（John Stuart Mill，1806-1873 年）和威廉·惠威尔（William Whewell，1794-1866 年）之间的辩论已成为 19 世纪方法论的经典辩论。虽然经常被描述为归纳主义和假设演绎主义之间的争论，但这两种方法在每一方的作用实际上都更加复杂。在假设-演绎法上，科学家们努力提出假设，从中可以推断出真正的观察结果——因此，假设-演绎法。因为惠威尔在他的方法叙述中同时强调假设和演绎，所以他可以被视为穆勒归纳主义的方便陪衬。然而，与惠威尔对科学方法的描述同样重要的是他所说的“基本对立面”。知识是客观（我们在周围世界中看到的）和主观（我们的思想对我们如何感知和理解我们所体验的贡献，他称之为基本理念）的产物。根据惠威尔的说法，这两个要素都是必不可少的，因此他批评康德过于关注主观，批评约翰·洛克（John Locke，1632-1704）和穆勒过于关注感官。Whewell 的基本思想可以是学科相对的。一个想法可以是基础的，即使它只是给定科学学科内的知识所必需的（例如，化学的化学亲和力）。这将基本思想与康德的直觉的形式和类别区分开来。
Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20th century (see section 3).
因此，澄清基本思想将是科学方法和科学进步的重要组成部分。Whewell 将这个过程称为“发现者的归纳”。这是继培根或牛顿之后的归纳法，但惠威尔试图通过强调思想在清晰而仔细地制定归纳假设中的作用来恢复培根的叙述。惠威尔的归纳不仅仅是对客观事实的收集。主观通过惠威尔所说的事实合理发挥作用，这是科学家的创造性行为，理论的发明。然后通过测试证实一个理论，其中更多的事实被纳入该理论，称为归纳一致性。惠威尔认为，这就是发现真正自然法则的方法：澄清基本概念，巧妙地发明解释，仔细测试。穆勒在他对惠威尔的批评中，以及其他将惠威尔塑造成假设-演绎主义观点先驱的人，似乎低估了这个发现阶段在惠威尔对方法的理解中的重要性（Snyder 1997a，b， 1999）。淡化发现阶段将成为 20 世纪初方法论的特征（见第 3 节）。
Mill, in his System of Logic, put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors (System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).
穆勒在他的《逻辑体系》中提出了一种更狭隘的观点，即归纳法是科学方法的本质。对 Mill 来说，归纳是首先寻找事件之间的规律。在这些规律中，有些将继续保持进一步观察，最终获得法律的地位。人们还可以在一个领域中发现的规律中寻找规律，即规律的规律。哪种“法律法”将适用取决于时间和学科，并且有待修订。一个例子是普遍因果律，穆勒提出了确定原因的具体方法——现在通常称为穆勒方法。这五种方法寻找感兴趣的现象中常见的情况，当现象存在时不存在的情况，或者两者一起变化的情况。穆勒的方法仍然被视为捕捉了关于寻找相关解释因素的实验方法的基本直觉（System of Logic （1843），见穆勒条目）。最终，Whewell 和 Mill 倡导的方法看起来很相似。两者都涉及对覆盖定律的归纳泛化。然而，在所获得的知识的必要性方面，他们却大相径庭;也就是说，在元方法论的层面上（参见 Whewell 和 Mill 条目的条目）。
3. Logic of method and critical responses 3. 方法逻辑和批判性回应
The quantum and relativistic revolutions in physics in the early 20th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.
20 世纪初物理学的量子和相对论革命对方法论产生了深远的影响。这两种理论的概念基础都被用来表明即使是关于空间、时间和身体的最看似安全的直觉也是不可行的。因此，关于自然世界的知识的确定性被认为是无法实现的。相反，人们寻求一种新的经验主义，这使得科学容易出错，但在理性上仍然是合理的。
Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.
出现了对科学家推理的分析，根据这些分析，科学方法中最重要的方面是检验和确认理论的手段。在方法论上，发现和证明的背景之间进行了区分。一方面，这种区别可以用作理论或假设的来源和方式的特殊性与科学家在评估理论并根据现有证据判断其充分性时使用的基本推理（无论他们是否意识到这一点）之间的楔子。总的来说，在 20 世纪的大部分时间里，科学哲学都集中在第二种背景上，尽管哲学家们在关注是确认还是反驳以及确认或反驳如何能够或不能带来的许多细节上存在分歧。到 20 世纪中叶，这些定义称义方法和上下文区分本身的尝试受到了压力。在同一时期，科学哲学迅速发展，因此从第 4 节开始，本条目将从对科学方法的主要历史处理转变为主要的主题处理。
3.1 Logical constructionism and Operationalism 3.1 逻辑建构主义和操作主义
Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)
逻辑和概率的进步为科学理论和实证方法的精心重建提供了可能性，最好的例子是鲁道夫·卡纳普 （Rudolf Carnap） 的《世界的逻辑结构》（The Logical Structure of the World，1928 年）。卡纳普试图证明科学理论可以被重建为一个正式的公理系统——即一个逻辑。该系统可以指代世界，因为它的一些基本句子可以解释为观察或操作，人们可以执行这些观察或操作来测试它们。理论系统的其余部分，包括使用理论或不可观察术语（如电子或力）的句子，要么是有意义的，因为它们可以简化为观察，要么它们具有纯粹的逻辑意义（称为分析，如数学恒等式）。这被称为意义的可验证性标准。根据该标准，任何既不分析也不可验证的陈述严格来说都是没有意义的。尽管 Carnap 在 1928 年赞同这一观点，但他后来认为它过于严格（Carnap 1956）。这个想法的另一个熟悉版本是 Percy William Bridgman 的操作主义。在《现代物理学的逻辑》（1927 年）中，布里奇曼断言，每个物理概念都可以根据人们为验证该概念的应用而执行的操作来定义。然而，即使像长度这样简单，也很容易使概念变得非常复杂（例如，测量非常小的长度）或不切实际（测量光年等长距离）。
Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se, but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4.[4]
Carl Hempel （1950， 1951） 对意义的可验证性标准的批评具有巨大的影响力。他指出，普遍的概括，如大多数科学定律，对标准并不严格意义。可验证性和操作主义似乎都过于严格，无法捕捉到标准的科学目标和实践。这些重建与实际科学实践之间的微弱联系以另一种方式受到批评。在这两种方法中，科学方法都被重新塑造为方法论的角色。例如，测量被视为赋予术语含义的方式。科学哲学家的目标不是理解方法本身，而是用它们来重建理论、它们的意义以及它们与世界的关系。然而，当科学家进行这些操作时，他们不会报告说他们这样做是为了给正式公理系统中的术语赋予意义。方法论与实际科学实践细节之间的这种脱节似乎违反了逻辑实证主义者和布里奇曼所致力于的经验主义。认为方法论应该与实践相对应的观点（在某种程度上）被称为历史主义或直觉主义。我们在第 3.4 节中讨论这些批评和回应。[4]
Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science.) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.
实证主义还必须与这样一种认识作斗争，即按照培根-牛顿-穆勒的思路，纯粹的归纳主义方法是站不住脚的。首先，没有纯粹的观察。所有的观察都是充满理论的。进行任何观察都需要理论，因此并非所有理论都可以单独从观察中得出。（参见 科学理论与观察 条目。即使提供观察基础，休谟也已经指出，如果不通过假设归纳法的成功来提出问题，就无法通过演绎来证明归纳结论的合理性。同样，实证主义试图分析如何通过对其实例的观察来证实概括，也受到了许多批评。Goodman （1965） 和 Hempel （1965） 都指出了标准确认叙述中固有的悖论。最近的尝试解释了观察如何用于证实科学理论，将在下面的第 4 节中讨论。
3.2. H-D as a logic of confirmation 3.2. H-D 作为确认逻辑
The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2, this method had been advanced by Whewell in the 19th century, as well as Nicod (1924) and others in the 20th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.
确认逻辑的非归纳分析的标准起点称为假设-演绎 （H-D） 方法。在最简单的形式中，表达某种假设的理论的句子被其真正的后果所证实。如第 2 节所述，这种方法在 19 世纪由 Whewell 提出，Nicod （1924） 和 20 世纪的其他人也提出了。通常，Hempel （1966） 对 H-D 方法的描述，以 Semmelweiss 确定产褥热原因的推理程序为例，被作为 H-D 的关键解释以及对 H-D 确认解释的批评的陪衬（例如，参见 Lipton （2004） 对最佳解释的推理的讨论;也是关于确认的条目）。Hempel 将 Semmelsweiss 的手术描述为检查解释产褥热原因的各种假设。一些假设与可观察的事实相冲突，可以立即被拒绝为错误。其他测试需要通过实验测试，推断如果假设为真（Hempel 称之为假设的测试含义），然后进行实验并观察测试含义是否发生。如果试验表明检验暗示为假，则可以拒绝假设。但是，如果实验表明检验含义是正确的，这并不能证明假设是正确的。对测试含义的确认并不能验证假设，尽管 Hempel 确实允许“它至少提供了一些支持、一些佐证或确认”（Hempel 1966：8）。这种支持的程度取决于支持证据的数量、种类和准确性。
3.3. Popper and falsificationism 3.3. 波普尔和证伪主义
Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality.)
另一种摆脱归纳推理困难的方法是卡尔·波普尔的批判理性主义或证伪主义（Popper 1959， 1963）。证伪是演绎性的，类似于 H-D，因为它涉及科学家从被检验的假设中推断出观察结果。然而，对波普尔来说，重要的一点不是成功预测对假设的确认程度。关键是确认（基于归纳推理）和证伪（可以基于演绎推理）之间的逻辑不对称。
Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.
波普尔强调，无论确认证据的数量如何，如果不犯下肯定结果的谬误，我们永远无法确定假设是正确的。相反，波普尔引入了确证的概念，作为衡量理论或假设在先前测试中存活的程度的指标，但并不意味着这也是衡量其为真的概率的指标。
Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.
波普尔还受到对马克思主义历史理论或精神分析等理论的科学地位的怀疑的激励，因此想要区分科学和伪科学。波普尔认为这与将科学与形而上学区分开来是一个重要的区别。后一种划分是许多逻辑经验主义者的主要关注点。波普尔使用证伪的概念在伪科学和真正的科学之间划清界限。科学之所以是科学，是因为它的方法涉及对理论进行严格的测试，这些测试提供了很高的失败可能性，从而反驳了理论。
A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.
对失败风险的承诺很重要。避免伪造很容易。如果理论的结果与观察不一致，则可以通过引入明确设计的辅助假设来添加异常，以挽救理论，即所谓的临时修改。波普尔在伪科学中看到了这个结果，其中特设理论似乎能够解释其应用领域中的任何事情。相比之下，科学是有风险的。如果观察表明一个理论的预测是错误的，那么这个理论就会被驳斥。因此，科学假设必须是可证伪的。不仅必须存在一些可能的观察陈述来证伪假设或理论，如果观察到（波普尔称这些为假设的潜在证伪者），那么对于波普尔的科学方法来说，定期真诚地尝试这种证伪是至关重要的。
The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).
一个假设的潜在证伪者越多，它的可证伪性就越大，假设声称的就越多。相反，没有证伪者的假设很少或根本没有主张。最初，波普尔认为这意味着仅仅为了挽救理论而引入临时假设不应该被视为好的科学方法。这些会破坏理论的证伪性。然而，波普尔后来认识到，修饰的引入（他称之为免疫接种）通常是科学发展的重要组成部分。对令人惊讶或明显证伪的观察结果做出回应往往会产生重要的新科学见解。波普尔自己的例子是观察到的天王星运动，它最初与牛顿的预测不一致。外行星的临时假设解释了这种分歧，并导致了进一步的可证伪预测。波普尔试图通过模糊可证伪和不可证伪之间的区别，以及说话而不是可测试性程度来调和这一观点（波普尔 1985：41f.）。
3.4 Meta-methodology and the end of method 3.4 元方法论和方法的结束
From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.
从 1960 年代开始，出现了持续的元方法论批评，将哲学焦点从科学方法上赶走。接下来简要介绍一下这些批评，并在条目末尾建议进一步阅读。
Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:
托马斯·库恩 （Thomas Kuhn） 的《科学革命的结构》（The Structure of Scientific Revolutions，1962 年）以科学哲学家们的一记著名的开枪开始：
History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)
如果将历史视为不仅仅是轶事或年表的宝库，那么它可能会对我们现在所拥有的科学形象产生决定性的转变。(1962: 1)
The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle.) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.
库恩认为需要改变的形象是许多逻辑实证主义者所寻求的非历史的、理性的重建，尽管卡纳普和其他实证主义者实际上对库恩的观点相当同情。库恩与他的其他同时代人，如 Feyerabend 和 Lakatos，都致力于对科学哲学采取更实证的方法。也就是说，科学史为科学哲学提供了重要的数据和必要的检查，包括任何科学方法理论。
The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.
根据库恩的说法，科学史揭示了科学发展发生在交替的阶段。在正常的科学研究中，科学界的成员遵守现有的范式。他们对范式的承诺意味着对要解决的谜题和可接受的解决难题的方法的承诺。只要在解决共同的难题方面取得稳步进展，人们对这个范式的信心就仍然存在。这个正常阶段的方法在一个学科矩阵（库恩后来的范式概念）中运行，其中包括解决问题的标准，并定义了该方法应该应用的问题范围。学科矩阵的一个重要部分是一组价值，它为科学方法提供了规范和目标。Kuhn 确定的主要价值是预测、解决问题、简单性、一致性和合理性。
An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place
正常科学的一个重要副产品是拼图的积累，这些拼图无法用当前范式的资源来解决。一旦这些异常的积累达到一定的临界质量，它就可以触发向新范式和正常科学新阶段的集体转变。重要的是，为科学方法提供规范和目标的价值观可能在此期间发生了变化。因此，方法可以相对于学科、时间或地点
Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).
Feyerabend 还将科学的目标是进步，但认为任何方法论处方都只会扼杀这种进步（Feyerabend 1988）。他的论点建立在重新审视公认的关于科学史的“神话”的基础上。事实证明，像伽利略这样的科学英雄对修辞和说服的依赖程度与他们对理性和论证的依赖程度一样。其他人，如亚里士多德，被证明在他们的观点中比他们受到赞扬的要合理和深远得多。因此，唯一能提供他认为足够自由的规则是空洞的“一切皆有可能”。更一般地说，即使是科学是追求知识和增加知识的最佳方式的方法论限制也过于严格。相反，费耶拉本德认为，科学实际上可能对自由社会构成威胁，因为它及其神话已经变得如此占主导地位（Feyerabend 1978）。
An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology.) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.
从 1970 年代开始，几位科学社会学家提出了一种更基本的批评，他们拒绝为科学的理性发展提供哲学解释和为非理性错误提供社会学解释的方法。相反，他们坚持一个对称论点，在这个论点上，任何关于科学知识如何建立的因果解释都需要对称，以相同的因果因素来解释真与假、理性与非理性、成功与错误（参见，例如，Barnes 和 Bloor 1982，Bloor 1991）。科学社会学的运动，如斯特朗计划，或更普遍的知识的社会维度和原因的运动，导致了对当代科学及其历史中详细案例研究的扩展和密切研究。（参见科学知识和社会认识论的社会维度条目。Latour和Woolgar （1979/1986），Knorr-Cetina（1981），Pickering （1984），Shapin和Schaffer（1985）的著名研究似乎证明，社会意识形态（在宏观尺度上）或个人互动和环境（在微观尺度上）是决定哪些信仰获得科学知识地位的主要因果因素。因此，在他们看来，对科学方法的解释性诉求并没有实证基础。
A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results.)
对科学方法的批评很晚，而且在很大程度上出乎意料地来自科学本身。从 2000 年代初开始，许多科学家试图复制已发表的实验结果，但无法做到这一点。可重复性和方法之间可能存在密切的概念联系。例如，如果可重复性意味着相同的科学方法应该产生相同的结果，并且所有科学结果都应该是可重复的，那么复制科学结果所需的一切都应该被称为科学方法。由于篇幅所限，我们只能观察到，只要可重复性是适当科学方法的预期结果，它就不是严格意义上的科学方法的一部分。
By the close of the 20th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.
到 20 世纪末，对科学方法的探索开始萎靡不振。Nola 和 Sankey （2000b） 在介绍他们关于方法的书时说：“对一些人来说，科学方法理论的整个想法是过去的辩论…”。
4. Statistical methods for hypothesis testing 4. 假设检验的统计方法
Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.
尽管哲学家在试图提供清晰的构象（或反驳）方法时遇到了许多困难，但在理解观察如何为给定理论提供证据方面仍然取得了重要进展。统计学工作对于理解如何用实证检验理论至关重要，近几十年来，出现了大量文献，试图用贝叶斯术语重新塑造确认。在这里，这些发展只能简要介绍，我们参考确认条目了解更多详情和参考资料。
Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19th century, criteria for the rejection of outliers proposed by Peirce by the mid-19th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce).
从 19 世纪开始，统计学在实验科学的方法论中发挥着越来越重要的作用。当时，统计学和概率论作为归纳推理的分析承担了方法论作用，并且试图将归纳的合理性建立在概率论的公理中，这一尝试在整个 20 世纪一直持续到现在。与此同时，统计理论本身的发展对实验方法产生了直接而巨大的影响，包括测量观察不确定性的方法，例如勒让德和高斯在 19 世纪初开发的最小二乘法，皮尔斯在 19 世纪中叶提出的拒绝异常值的标准， 以及 Gosset（又名 “学生”）、Fisher、Neyman 和 Pearson 等人在 1920 年代和 1930 年代开发的显著性测试。
These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.
统计学的这些发展反过来又导致了统计学家和科学哲学家之间关于如何看待假设检验过程的反思性讨论：它是否是一个严格的统计推断，可以提供对检验假设的置信度的数值表达，或者它是否应该被视为在也涉及价值成分的不同行动方案之间做出的决定。这导致了 Fisher 与 Neyman 和 Pearson 之间的重大争论（特别参见 Fisher 1955、Neyman 1956 和 Pearson 1955，以及对争议的分析，例如 Howie 2002、Marks 2000、Lenhard 2006）。在 Fisher 看来，假设检验是一种何时接受或拒绝统计假设的方法，即如果假设是正确的，如果该证据相对于其他可能的结果来说不太可能，则应通过证据拒绝假设。相比之下，根据 Neyman 和 Pearson 的观点，在决定假设时，错误的后果也必须发挥作用。在介绍拒绝真假设（I 类错误）和接受假假设（II 类错误）的错误之间的区别时，他们认为，根据错误的后果来决定避免拒绝真假设还是接受假假设更重要。因此，Fisher 的目标是一种归纳推理理论，该理论能够在假设中实现置信度的数字表达。对他来说，重要的是寻求真理，而不是效用。相比之下，Neyman-Pearson 方法提供了一种归纳行为策略，用于决定不同的行动方案。在这里，重要的一点不是假设是否真实，而是一个人是否应该表现得好像它是真的一样。
Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.
在哲学文献中也可以找到类似的讨论。一方面，Churchman （1948） 和 Rudner （1953） 认为，由于科学假设永远无法完全验证，因此对科学推理方法的完整分析包括道德判断，科学家必须决定证据是否足够有力，或者概率是否足够高以保证接受假设。 这又将取决于在接受或拒绝假设时犯错的重要性。其他人，如 Jeffrey （1956） 和 Levi （1960） 不同意，而是捍卫了一种价值中立的科学观，科学家在评估其推断的正确性时，应该将他们的态度、偏好、气质和价值观括起来。有关科学哲学中这种无价值理想及其历史发展的更多详细信息，请参阅 Douglas （2009） 和 Howard （2003）。有关科学中价值观作用的广泛案例研究，请参见例如Elliott & Richards 2017。
In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation.
近几十年来，关于通过统计推理评估概率假设的哲学讨论主要集中在贝叶斯主义上，贝叶斯主义将概率理解为在给定可用信息的情况下衡量一个人对事件的信念程度的指标，以及频率主义，相反，它将概率理解为可重复事件的长期频率。因此，对于贝叶斯来说，概率是指知识的状态，而对于频率论者来说，概率是指事件的频率（例如，参见 Sober 2008 年第 1 章，对贝叶斯主义和频率论以及可能性论的详细介绍）。贝叶斯主义旨在提供信念修正的可量化算法表示，其中信念修正是先前信念（即背景知识）和传入证据的函数。贝叶斯主义采用基于贝叶斯定理的规则，贝叶斯定理是与条件概率相关的概率演算定理。特定假设为真的概率被解释为科学家的信念或可信度。在一项证据（比如说观察）为真的情况下，假设将是真的概率和程度的信念也会存在。贝叶斯主义规定，如果证据实际上被观察到，科学家将他们对假设的信念更新为那个条件概率是合理的（例如，参见Sprenger和Hartmann 2019对贝叶斯科学哲学的全面处理）。频率主义起源于 Neyman 和 Person 的工作，旨在提供降低长期错误率的工具，例如 Mayo （1996） 开发的错误统计方法，该方法侧重于实验者如何通过建立一系列程序来避免 I 型和 II 型错误，这些程序在存在错误时检测到错误。贝叶斯主义和频率主义都是随着时间的推移而发展起来的，它们被不同的支持者以不同的方式解释，它们与以前的批评与定义科学方法的尝试的关系被支持者和批评者以不同的方式看待。
5. Method in Practice 5. 实践方法
Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.
正如我们所看到的，对科学实践的关注本身并不新鲜。然而，最近科学哲学的转向实践可以被视为对 20 世纪后期科学哲学方法悲观主义的纠正，以及社会学和理性主义对科学知识的解释之间的和解。这项工作的大部分内容将方法视为详细和特定于上下文的问题解决程序，而方法分析同时是描述性的、批判性的和咨询性的（参见 Nickles 1987 对这一观点的阐述）。以下部分包含对一些实践重点的调查。在本节中，我们完全转向主题而不是时间顺序。
5.1 Creative and exploratory practices 5.1 创造性和探索性实践
A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20th century (see section 2) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.
在 20 世纪上半叶的科学哲学中，发现和证明的背景之间的区别存在一个问题，即在科学活动中无法清楚地看到这种区别（见 Arabatzis 2006）。因此，近几十年来，人们已经认识到，对概念创新和变化的研究不应局限于心理学和科学社会学，而且也是科学哲学应该解决的科学实践的重要方面。寻找推动概念创新的实践，使哲学家们既考察了科学家的推理实践，也考察了不狭隘地针对检验假设的广泛实验实践，即探索性实验。
Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)
Nersessian （2008） 研究了历史和当代科学家的推理实践，认为新的科学概念是通过系统推理构建为特定问题的解决方案，而类比、视觉表示和思维实验是采用的重要推理实践之一。这些无处不在的推理形式是可靠的——但也是容易出错的——概念发展和变化的方法。在她看来，基于模型的推理包括模型的构建、模拟、评估和调整的周期，这些周期作为要解决的目标问题的临时解释。通常，此过程将导致修改或扩展，以及新的模拟和评估周期。然而，Nersessian 也强调基于模型的创造性推理不能作为简单的配方应用，并不总是有效的解决方案，即使是它最典型的用法也可能导致不正确的解决方案。（内尔塞斯 2008：11）
Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is
因此，虽然一方面她同意许多以前的哲学家的观点，即没有发现的逻辑，但发现可以来自理性的过程，因此科学实践的很大一部分是
the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)
创造概念，通过这些概念来理解、构建和交流物理现象…（内尔塞斯 1987：11）
Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.
同样，像Darden（1991）和Bechtel & Richardson（1993，学者们对启发式发现和理论构建的研究将科学呈现为解决问题的方式，并将科学问题解决作为一般问题解决的特例进行研究。他们主要借鉴生物科学的案例，大部分重点放在生成、评估和修订复杂系统机械解释的推理策略上。
Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa, exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.
针对上下文区分的另一个方面，即实验的主要作用是根据 H-D 模型检验理论假设的传统观点，其他科学哲学家认为实验可以发挥额外的作用。引入探索性实验的概念是为了描述由获得经验规律的愿望驱动的实验，并开发可以描述这些规律的概念和分类（Steinle 1997,2002;Burian 1997 年;Waters 2007 年））。然而，理论驱动实验和探索性实验之间的区别不应被视为鲜明的区别。理论驱动的实验并不总是针对检验假设，也可能针对各种事实收集，例如确定数值参数。反之亦然，探索性实验通常以各种方式受到理论的影响，因此并非没有理论。相反，在探索性实验中，研究现象时，不会首先根据关于现象的现有理论来限制实验的可能结果。
The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).
分子生物学和邻近领域高通量仪器的发展催生了一种特殊类型的探索性实验，可以收集和分析非常大量的数据，这些新的“组学”学科通常被认为代表了与假设驱动科学理想的决裂（Burian 2007;Elliott 2007 年;Waters 2007 年;O’Malley 2007 年），而是被描述为数据驱动的研究（Leonelli 2012 年;Strasser 2012） 或作为一种特殊的“便利实验”，其中许多实验仅仅是因为它们执行起来非常方便 （Krohs 2012）。
5.2 Computer methods and ‘new ways’ of doing science 5.2 计算机方法和科学研究的“新方法”
The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?
刚才描述的组学领域是可能的，因为计算机能够在合理的时间内处理所需的大量数据。计算机允许更复杂的实验（更高的速度、更好的过滤、更多的变量、复杂的协调和控制），但通过建模和模拟，本身可能构成一种实验形式。在这里，我们也可以提出方法与实践的一般问题的一个版本：使用计算机的实践是从根本上改变了科学方法，还是仅仅提供了一种更有效的实施标准方法的手段？
Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.
由于计算机可用于自动化测量、量化、计算和统计分析，而由于实际原因，这些操作无法以其他方式进行，因此根据实验得出结论所涉及的许多步骤现在都在“黑匣子”内进行，无需人类的直接参与或意识。这具有认识论意义，关于我们能知道什么，以及我们如何知道它。因此，为了对结果充满信心，计算机方法要经过验证和确认测试。
The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.
在计算机模拟的情况下，验证和确认之间的区别最容易描述。在典型的计算机模拟场景中，计算机用于对没有解析解的微分方程进行数值积分。这些方程是科学家用来表示正在研究的现象或系统的模型的一部分。验证计算机模拟意味着检查模型的方程是否被正确近似。验证模拟意味着检查模型的方程是否足以满足人们想要基于该模型进行的推断。
A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.
已经提出了许多与计算机模拟相关的问题。将有效性和验证确定为测试方法受到批评。Oreskes et al. （1994） 提出了对“验证”的担忧，因为它暗示了演绎推理，可能会导致对模拟结果的过度自信。区别本身可能太明显了，因为模拟测试的实际实践在两者之间混合和来回移动（Weissart 1997;帕克 2008a;Winsberg 2010 年）。计算机模拟似乎确实具有非归纳性，因为它们运行的原则是由程序员内置的，并且模拟的任何结果都遵循这些内置原则，因此这些结果原则上可以从程序代码及其输入中推断出来。因此，已经检查了模拟作为实验的地位（Kaufmann 和 Smarr 1993;汉弗莱斯 1995 年;休斯 1999 年;Norton 和 Suppe 2001）。这些文献考虑了这些实验的认识论：我们可以通过模拟学到什么，以及将这些知识应用于“真实”世界可以给出的各种理由。（梅奥 1996 年;Parker 2008b）。如前所述，计算机模拟的部分优势来自于这样一个事实，即无需实验者/模拟器直接观察即可进行大量计算。同时，其中许多计算都是在理想情况下直接执行的计算的近似值。这两个因素都为从模拟中观察到的内容得出的推论带来了不确定性。
For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science).
由于上述许多原因，计算机模拟似乎并不明确属于实验或理论领域。相反，它们似乎至关重要地涉及两者的各个方面。这导致一些作者，如 Fox Keller （2003： 200） 认为我们应该将计算机模拟视为一种“性质不同的科学方式”。一般来说，文献倾向于遵循 Kaufmann 和 Smarr （1993） 将计算机模拟称为科学方法的“第三种方式”（理论推理和实验实践是前两种方式）。还应该注意的是，围绕这些问题的辩论往往集中在物理科学中典型的计算机模拟形式上，其中模型基于动力学方程。其他形式的模拟可能没有相同的问题，或者有自己的问题。
In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data.
近年来，机器学习技术的快速发展促使一些学者认为科学方法已经“过时”（Anderson 2008，Carrol 和 Goodstein 2009）。这导致了关于数据驱动和假设驱动研究的相对价值的激烈辩论。关于这个话题的详细处理，我们参考了科学研究和大数据条目。
6. Discourse on scientific method 6. 关于科学方法的论述
Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.
尽管在哲学上存在分歧，但科学方法的理念仍然在科学和整个社会的许多不同主题的当代话语中占据突出地位。通常，引用科学方法的方式要么是传达所有科学的单一、普遍方法特征的传说，要么是授予特定方法或一组方法作为特殊的“黄金标准”的特权，通常是指特定的哲学家来证明这些主张的。当需要区分科学和其他活动时，或者为了证明科学所传达的特殊地位的合理性时，通常也会出现关于科学方法的讨论。在这些领域中，确定科学努力的一套方法特征的哲学尝试与科学哲学的经典划分问题以及对科学知识的社会维度和科学在民主社会中的作用的哲学分析密切相关。
6.1 “The scientific method” in science education and as seen by scientists 6.1 科学教育中的“科学方法”和科学家所看到的
One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002).[5] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)
单一的、普遍的科学方法的传奇特别强大的环境之一是科学教育（例如，参见 Bauer 1992;McComas 1996 年;Wivagg & Allchin 2002）。[5] 通常，“科学方法”在教科书和教育网页中作为一个固定的四到五个步骤程序呈现，从观察和描述现象开始，逐步发展到形成解释现象的假设，设计和进行实验来检验假设，分析结果，最后得出结论。在各级科学教育的教育材料中都可以找到这种对通用科学方法的引用（Blachowicz 2009），大量研究表明，通用和通用科学方法的理念往往是学生和教师科学概念的一部分（参见，例如，Aikenhead 1987;Osborne 等人，2003 年）。作为回应，有人认为科学教育需要更多地关注关于科学本质的教学，尽管对于这是通过学生主导的调查、当代案例还是历史案例来最好地进行，人们的看法存在分歧（Allchin，Andersen & Nielsen 2014）
Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of
虽然偶尔会提到 H-D 方法，但单一、普遍的科学方法的科学教育传奇的重要历史根源是美国哲学家和心理学家杜威在《我们如何思考》（1910 年）中对探究的描述，以及英国数学家卡尔·皮尔逊在《科学语法》（1892 年）中对科学的描述。根据杜威的叙述，探究分为五个步骤
(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)
（i） 感到困难，（ii） 它的位置和定义，（iii） 提出可能的解决方案，（iv） 通过推理建议的方位来发展，（v） 进一步的观察和实验导致它被接受或拒绝。（杜威 1910：72）
Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.
同样，根据皮尔逊的说法，科学调查始于对数据的测量和观察其校正和顺序，从而在创造性想象的帮助下发现科学定律。这些定律必须受到批评，而它们的最终接受将对“所有正常构成的思想”具有同等效力。杜威和皮尔逊的叙述都应该被看作是广义的抽象研究，而不限于科学领域——尽管杜威和皮尔逊都将他们各自的叙述称为“科学方法”。
Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures.[6] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how
有时，科学家们会对一种简单而独特的科学方法做出笼统的陈述，例如，费曼在 1964 年康奈尔信使讲座的最后一场讲座中提出的简化版猜想和反驳方法就是例证。[6]然而，正如科学家们经常得出与最近的科学哲学相同的结论，即没有任何独特的、易于描述的科学方法。例如，物理学家和诺贝尔奖获得者温伯格在论文《科学的方法…And Those That We Live“（1995 年）如何
The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)
科学成功的标准随着时间的推移而变化的事实不仅使科学哲学变得困难;它还给公众对科学的理解带来了问题。我们没有固定的科学方法来团结和捍卫。(1995: 8)
Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)
对科学家关于他们的方法概念的采访研究表明，科学家们经常发现很难弄清楚是否有可用的证据证实他们的假设，而且关于方法的一般观念和指导研究进行的具体策略之间没有直接的翻译（Schickore & Hangel 2019，Hangel & Schickore 2017）
6.2 Privileged methods and ‘gold standards’ 6.2 特权方法和“黄金标准”
Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.
对科学方法的引用也经常被用来论证特定活动的科学性质或特殊地位。主张以简单而独特的科学方法作为划分标准的哲学立场，例如波普里式的证伪，经常吸引那些认为他们需要捍卫自己的实践领域的实践者。例如，在许多关于补充和替代医学 （CAM） 的文献中，大量引用猜想和反驳作为科学方法——此外，CAM 作为传统生物医学的替代品，需要发展自己的方法与科学的方法不同。
Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.
同样在主流科学中，在关于学科和领域的内部层次结构的争论中也使用了对科学方法的引用。一个常见的论点是，基于 H-D 方法的研究优于基于观察归纳的研究，因为在演绎推理中，结论必然来自前提。（例如，参见 Parascandola 1998 年，分析了与实验室科学相比，这一论点是如何被用来降低流行病学的等级的。同样，基于对美国国立卫生研究院 （NIH）、美国国家科学基金会 （NSF） 和英国生物医学科学研究实践 （BBSRC） 等主要资助机构实践的审查，O’Malley 等人（2009 年）认为，资助机构似乎倾向于坚持科学的主要活动是检验假设的观点。 而描述性和探索性研究被视为仅仅是准备活动，只有在它们推动假设驱动的研究时才有价值。
In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).
在某些科学领域，学术出版物的结构可能传达出一个整洁而线性的探究过程的印象，从陈述问题、设计回答问题的方法、收集数据到从数据分析中得出结论。例如，大多数生物医学期刊中被称为 IMRAD 格式（引言、方法、结果、分析、讨论）的出版物编纂格式被期刊编辑明确描述为“不是一种随意的出版格式，而是科学发现过程的直接反映”（参见所谓的“温哥华建议”，ICMJE 2013：11）。然而，科学出版物通常不反映所报告的科学结果的产生过程。例如，在挑衅性的标题“科学论文是骗局吗？”下，Medawar 认为科学论文通常会歪曲结果的产生方式（Medawar 1963/1996）。哲学家、历史学家和科学社会学家也提出了类似的观点（Gilbert 1976;Holmes 1987;Knorr-Cetina 1981 年;Schickore 2008 年;Suppe 1998），他们认为科学家的实验实践是混乱的，而且往往不遵循任何可识别的模式。他们认为，研究成果的发布是对这些活动的回顾性重建，这些活动通常不保留这些活动的时间顺序或逻辑，而往往是为了屏蔽潜在的批评而构建的（参见 Schickore 2008 对这项工作的评论）。
6.3 Scientific method in the court room 6.3 法庭上的科学方法
Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that
关于科学方法的哲学立场也进入了法庭，尤其是在美国，法官在决定何时赋予科学专家证词特殊地位时借鉴了科学哲学。一个关键案例是 Daubert 诉 Merrell Dow Pharmaceuticals （92–102， 509 U.S. 579， 1993）。在本案中，最高法院在其 1993 年的裁决中认为，审判法官必须确保专家证词可靠，在此过程中，法院必须查看专家的方法，以确定所提供的证据是否真的是科学知识。此外，在提到波普尔和亨佩尔的作品时，法院指出：
ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)
通常，在确定理论或技术是否是科学知识时需要回答的一个关键问题…是它是否可以（并且已经）测试。（Blackmun 法官，Daubert v. Merrell Dow Pharmaceuticals;参见其他互联网资源以获取该意见的链接）
But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).
但是，正如Haack （2005a，b， 2010） 和Foster & Hubner （1999）所论述的，通过将一个证词是否可靠的问题等同于特殊方法所表明的它是否科学的问题，法院产生了波普尔和亨佩尔哲学的不一致混合，这在后来的案件裁决中导致了相当大的混乱，这些裁决借鉴了Daubert案（参见Haack 2010的详细阐述）。
6.4 Deviating practices 6.4 偏离做法
The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as
识别科学方法的困难也反映在识别以不当应用科学方法的形式存在的科学不端行为的困难。定义科学不端行为的最早也是最有影响力的尝试之一是 1989 年的美国定义，该定义将不端行为定义为
fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community. (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)
捏造、伪造、剽窃或其他严重偏离科学界普遍接受的做法。（联邦法规，第 50 部分，A 子部分，1989 年 8 月 8 日，斜体字添加）
However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it
然而，“其他严重偏离的做法”条款受到了严厉批评，因为它可能被用来压制创造性或新颖的科学。例如，美国国家科学院在其报告《负责任科学》（1992 年）中指出，它
wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)
希望阻止仅根据科学家使用新颖或非正统的研究方法而对科学家提出不当行为投诉的可能性。（NAS：27）
This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).
因此，该条款后来从定义中删除。有关科学行为的关键哲学文献的条目，请参见Shamoo & Resnick （2009）。
7. Conclusion 7. 结论
The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.
自现代科学诞生以来，科学成功的来源问题一直是哲学的核心。如果更普遍地从认识论的角度来看，科学方法是整个哲学史的一部分。在那段时间里，科学及其实践者可能采用的任何方法都发生了巨大变化。今天，许多哲学家已经扛起了多元主义或实践的旗帜，专注于实际上对科学方法的细粒度和上下文限制的检查。其他人则希望改变观点，以便对我们称之为科学的活动的特点提供新的一般性解释。
One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.
Hoyningen-Huene （2008， 2013） 最近提出了这样一种观点，他从科学哲学史上认为，在用科学的方法描述科学的三个漫长阶段之后，我们现在正处于一个阶段，对积极科学方法存在的信念已经被侵蚀，剩下的用来描述科学的只是它的易错性。首先是从柏拉图和亚里士多德到 17 世纪的一个阶段，科学知识的特殊性是通过明显的公理证明来确立的绝对确定性;接下来是直到 19 世纪中叶的一个阶段，在这个阶段，建立科学知识确定性的方法已经被推广到包括归纳程序。在持续到 20 世纪最后几十年的第三阶段，人们认识到经验知识是容易出错的，但由于其独特的生产方式，它仍然被赋予了特殊的地位。但是现在到了第四阶段，根据 Hoyningen-Huene 的说法，历史和哲学研究表明，“具有第二和第三阶段所假设特征的科学方法不存在”（2008：168），哲学家和科学历史学家之间不再对科学的本质达成任何共识。对 Hoyningen-Huene 来说，这种立场太消极了，因此他敦促重新提出关于科学本质的问题。他自己对这个问题的回答是，“科学知识与其他类型的知识不同，尤其是日常知识，主要在于更加系统”（Hoyningen-Huene 2013：14）。系统性可以有几个不同的维度：其中包括更系统的描述、解释、预测、对知识主张的捍卫、认识的联系、完整性的理想、知识的产生、知识的表示和批判性话语。因此，科学的特点是更加小心地排除可能的替代解释，对预测所依据的数据进行更详细的阐述，在检测和消除错误来源时更加小心，与其他知识的联系更加清晰，等等。在这个立场上，科学的特点不是所采用的方法为科学所独有，而是这些方法被更谨慎地使用。
Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.
Haack （2003） 提供了另一种类似的方法。与 Hoyningen-Huene 类似，她从对最近她所谓的旧尊重主义和新犬儒主义之间冲突的不满出发。老式的尊重主义立场是，科学通过积累由经验证据证实的真实理论或通过根据基本陈述测试猜想而进行演绎而取得归纳进步;而新犬儒学派的立场是，科学没有认识论的权威，也没有独特的理性方法，而只是政治。哈克坚持认为，与新犬儒主义者的观点相反，存在客观的认识标准，并且科学在认识论上有一些特殊之处，尽管老派尊重主义者以错误的方式描绘了这一点。相反，她提供了一个新的批判性常识主义解释，即良好、有力、支持性的证据和实施良好、诚实、彻底和富有想象力的调查的标准并不仅限于科学，而是我们评判所有探究者的标准。从这个意义上说，科学与其他类型的探究在种类上没有区别，但它可能在需要广泛而详细的背景知识和熟悉只有专家才能具备的技术词汇的程度上有所不同。
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via:
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Scientific Method (Stanford Encyclopedia of Philosophy)