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统计推断Statistical inference机械部件通常被设计为精确地配合在一起。例如，如果一个轴被设计成在一个孔中 “滑动配合”，轴必须比孔小一点。(传统的公差可能表明，所有的尺寸都在这些预定的公差之内。然而，对实际生产的过程能力研究可能显示出具有长尾巴的正态分布）。轴和孔的尺寸通常会形成具有一些平均值（算术平均值）和标准差的正态分布。
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统计代写|统计推断作业代写Statistical inference代考|Intervening Developments
It would be extraordinary if we could spend half a century chronicling the development of a very active discipline without that history being constantly rendered obsolete by changes in the field. Yet that is very nearly what happened in the writing of this book. The criticisms that were raised in the last 50 years, occasionally by prominent psychologists, were not new and had no more effect than the original ones. Within the past two decades, however, a shift has been discernible, and it parallels that occurring in response to Jacob Cohen’s work on power (1962). Up to that time, power was a neglected concept of statistical inference (Chap. 9). It was difficult and for that reason omitted from many texts; many of the textbooks which did discuss it made mistakes; and it was generally ignored in research work. Cohen suggested that under the circumstances we might expect that the power of our experiments to be low, and he estimated that perhaps half of our experiments would be capable of detecting an effect if there were one. It is interesting to ponder why scientists are so impervious to logic; but Cohen’s empirical, numerical demonstration had an impact-a very gradual one: Within 30 or 40 years, the federal government was requiring power analysis in grant proposals.
A similar logic applies ro the replication problem, with the fact that $p$ values are such poor arbiters of truth. It was Ioannides’ $(2005$ a; b) quantitative estimate, similar to Cohen’s, that perhaps half of all published research findings are false that finally caused medical and psychological research workers to take notice. Within a few years, Brian Nosek (Open Science Collaboration, 2015) had instigated a quantitative, empirical demonstration of Ioannides’ claim. In a massive collaborative effort, they selected for replication 100 articles in the year 2008 from 3 premier
journals in psychology: Psychological Science, Journal of Personality and Social Psychology, and Journal of Experimental Psychology: Learning, Memory, and Cognition. They achieved a replication, in the sense of also getting significant results, in 39. The whole fantastically expensive enterprise of psychological and medical research is thus arguably less than worthless, because it leads to reliance on findings which are known not to be reliable: better not to have produced the research in the first place.
Not content with this many-studies/one-replication demonstration, Nosek (Silberzahn et al., 2018) also spearheaded a one-study/many-replications approach, in which a single, complex dataset was sent to 29 analytic teams, with the same research question. The dataset was a realistic one: The structure was complex enough to offer several different analytic possibilities, and the data were ambiguous enough that choices of analytic strategy could make a difference in the results. The teams used 21 unique sets of covariates, though 1 author thought the inclusion of 1 particular covariate by some teams was indefensible and should be counted as a mistake. Twenty of the teams got a significant result, with widely varying effect sizes; 9 did not. That variation is pretty much what a seasoned analyst would have expected, though the article still caused quite a stir.
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|Some Qualifcations Regarding the Historical Argument
The “mythical” nature of the concept of statistical inference itself virtually necessitates a historical analysis, not only of statistical inference but also of the concept of probability on which it is based. Treatises of probability-like J. R. Lucas’s (1970), for example, to pick one virtually at random—which begin, as I once aspired to, from reflection on the modern concept are doomed to a certain futility, just because they necessarily accept its crucial presuppositions and are therefore blind to them. The very deep confusion over the meaning, extensional as well as intensional, of schizophrenia is similarly pathognomonic, perhaps, of a mythical concept that should have died or shouldn’t have been born. Like statistical inference and probability, the concept of schizophrenia survives less because of what it actually designates than what it promises; and to get at the root of these concepts entails a willingness, at least in principle, to relinquish that promise. Not by asking directly what statistical inference, or probability, means, in short, but rather how these concepts came about, can we can discern whatever meaning they may be said to have. Outside that context, the orthodox theory of statistical inference, in particular, is simply too arbitrary and Procrustean a framework to make sense of.
One of two opposite objections may come to mind at this point. (If both do, there is a diagnosis for that, and welcome to the club.) One is the observation that revealing the history of an idea is not the same thing as refuting it. To show, as I do, that our favored data analytic tools derived from misguided attempts to mathematize induction does not invalidate them per se as data analytic tools, any more than the utility of Post-It ${ }^{\mathrm{TM}}$ notes is nullified by the failure of their adhesive as a glue. Nevertheless, this book may, if it is successful, help to bring about change in the same way that insight-oriented psychotherapy sometimes does: Once we see clearly why we have been doing certain things, it becomes harder to keep doing them.
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|An Ad Hominem Ipsum
A final issue in the construction of history has to do with the fact that the intellectual development I have sketched so far, as well as most of the history in this book, is what could be called conscious history: an account of what various people thought. But the conscious level is not necessarily where the action is; we need to attend as well to the cultural and psychological conditions which make possible or difficult a given line of development, the usually invisible background conditions which phenomenology aims to uncover. “Unconscious” history is a tricky endeavor, however. It easily becomes reductionist, or determinist, and is ordinarily taken as undermining, or invalidating, the conscious arguments; psychoanalysis itself, in fact, is conceived as a “hermeneutics of suspicion” (Ricoeur, 1970). The argumentum ad hominem is not merely a logical fallacy; it also readily, and understandably, gives offense. But I don’t know that, in undertaking such an inquiry, we are committed to the cynical view that the unconscious level necessarily holds the truth, which conscious discourse is frantically concerned to cover up. We can surely take account of factors promoting or hindering certain lines of thought without seeing them as decisive. I shall be endeavoring, at any rate, to listen with one ear for psychological and cultural, as well as philosophical, themes in the development of probability and statistical inference. At the cultural level, for example, the question arises of why the concepts of probability and statistical inference should have appeared in England and France rather than, say, Italy or Japan. Questions of why something didn’t happen can be difficult, however; I will have little to say about them except in Chap. 3 , where the story begins.
It may be useful to include here a brief ad hominem ipsum, with illustrative examples of such factors in the genesis of the present book. A “conscious” history will tend to make it sound as though any curious person, at least in a given culture, should have ended up in the same place. However strange it may seem to me, that is clearly far from being so in my own case. Identifying a few additional influences, conditions, and supports affords also the opportunity for some appropriate acknowledgments. Some of these are “unconscious” only in the sense that I was not aware of their role at the time, and their relevance was general rather than specific to the content of this book.
Chronologically the first influence, of course, was my parents. I confess that acknowledgments of the author’s parents had always struck me as puerile and trite; it is also true that my parents, not being intellectuals, made no direct contribution to this book. But over time it has become clearer what extraordinary support I had been taking for granted. My mother, whose intelligence, in more favorable circumstances, would have allowed her to go much farther in that direction than being a crack stenographer, had an independent streak which perhaps found in me its greatest opportunity for expression. And I still marvel at how my father, with a tenth-grade education and solidly conventional views, could have managed to be so supportive of whatever I became. I would not have done nearly so well with a son whose main interests were hunting, football, and boxing. When I see how much more difficult it appears to be for many people to hold views fundamentally different from most of those around them, I think I may know one of the reasons.
统计推断学代写
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|INTERVENING DEVELOPMENTS
如果我们能用半个世纪的时间来记录一门非常活跃的学科的发展，而该历史不会因为该领域的变化而不断过时，那将是非凡的。然而，这几乎就是本书写作过程中发生的事情。在过去的 50 年中，偶尔由著名心理学家提出的批评并不新鲜，也没有比最初的批评更有效。然而，在过去的二十年里，一种转变是显而易见的，它与雅各布·科恩关于权力的工作所发生的类似1962. 直到那个时候，权力还是一个被忽视的统计推断概念CH一种p.9. 这很困难，因此在许多文本中都被省略了；许多确实讨论过它的教科书都犯了错误；在研究工作中通常被忽略。科恩建议，在这种情况下，我们可能会认为我们的实验的能力很低，他估计如果有一个效果，我们的实验中可能有一半能够检测到效果。思考为什么科学家如此不受逻辑影响是很有趣的。但科恩的经验性数字论证产生了影响——这是一个非常渐进的影响：在 30 或 40 年内，联邦政府要求在拨款提案中进行权力分析。
类似的逻辑适用于复制问题，事实上p价值观是如此糟糕的真理仲裁者。是约阿尼德斯的(2005一种; b) 定量估计，类似于科恩的估计，可能所有已发表的研究结果中有一半是错误的，这最终引起了医学和心理学研究人员的注意。几年之内，Brian Nosek这p和n小号C一世和nC和C这一世一世一种b这r一种吨一世这n,2015对 Ioannides 的主张进行了定量的、经验性的论证。在大规模的合作努力中，他们在 2008 年从 3 个 Premier 中选择复制 100 篇文章
心理学期刊：心理科学、人格与社会心理学杂志和实验心理学杂志：学习、记忆和认知。他们在 39 中实现了复制，也获得了显着的成果。因此，整个心理和医学研究的成本极其昂贵的事业可以说不是毫无价值，因为它导致依赖于已知不可靠的发现：更好一开始就没有进行研究。
不满足于这种多研究/单复制演示，Nosek小号一世一世b和r和一种Hn和吨一种一世.,2018还率先采用了一项研究/多次重复的方法，其中将一个单一的、复杂的数据集发送给 29 个具有相同研究问题的分析团队。数据集是一个现实的数据集：结构足够复杂，可以提供几种不同的分析可能性，并且数据足够模糊，分析策略的选择可能会对结果产生影响。这些团队使用了 21 组独特的协变量，尽管 1 位作者认为某些团队包含 1 个特定的协变量是站不住脚的，应该算作一个错误。20 个团队获得了显着的结果，效果大小差异很大；9 没有。这种变化几乎是经验丰富的分析师所预期的，尽管这篇文章仍然引起了不小的轰动。
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|SOME QUALIFCATIONS REGARDING THE HISTORICAL ARGUMENT
统计推断概念本身的“神话”性质实际上需要进行历史分析，不仅对统计推断而且对它所依据的概率概念进行分析。JR Lucas 的概率论论文1970例如，几乎随机选择一个——正如我曾经渴望的那样，从对现代概念的反思开始，注定是徒劳的，只是因为它们必然接受它的关键预设，因此对它们视而不见。对精神分裂症的意义、外延和内涵的非常深刻的混淆也许同样是一个应该死或不应该出生的神话概念的特征。与统计推断和概率一样，精神分裂症的概念之所以能幸存下来，与其说是它实际指定的东西，不如说是它所承诺的东西。并且要找到这些概念的根源，至少在原则上需要放弃这一承诺。简而言之，不是直接询问统计推断或概率意味着什么，而是询问这些概念是如何产生的，我们能不能辨别出它们可能具有的任何含义。在这种情况下，特别是正统的统计推断理论简直太武断了，Procrustean 是一个难以理解的框架。
此时可能会想到两种相反的反对意见之一。一世Fb这吨Hd这,吨H和r和一世s一种d一世一种Gn这s一世sF这r吨H一种吨,一种nd在和一世C这米和吨这吨H和C一世你b.一个是观察到揭示一个想法的历史与驳斥它不是一回事。正如我所做的那样，我们最喜欢的数据分析工具源于错误地尝试将归纳数学化并不会使它们本身作为数据分析工具失效，就像便利贴的效用一样吨米笔记因粘合剂失效而失效。尽管如此，如果这本书成功，它可能会帮助带来改变，就像洞察力导向的心理治疗有时所做的那样：一旦我们清楚地看到为什么我们一直在做某些事情，继续做这些事情就变得更加困难。
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|AN AD HOMINEM IPSUM
历史建构的最后一个问题与这样一个事实有关，即我迄今为止所勾勒的知识发展，以及本书中的大部分历史，可以称为有意识的历史：对各种人的想法的描述. 但意识层面不一定是行动所在；我们还需要关注使特定发展路线成为可能或困难的文化和心理条件，现象学旨在揭示的通常不可见的背景条件。然而，“无意识”历史是一项棘手的工作。它很容易变成还原论者或决定论者，并且通常被认为是破坏或使有意识的论点无效；事实上，精神分析本身被认为是“怀疑的解释学”R一世C这和你r,1970. 人身论点不仅仅是一个逻辑谬误；它也很容易并且可以理解地冒犯。但我不知道，在进行这样的调查时，我们是否致力于一种愤世嫉俗的观点，即无意识层面必然拥有真理，而有意识的话语却疯狂地想要掩盖它。我们当然可以考虑促进或阻碍某些思路的因素，而不认为它们是决定性的。无论如何，我将努力用一只耳朵倾听概率和统计推断发展中的心理和文化以及哲学主题。例如，在文化层面，问题出现了，为什么概率和统计推断的概念应该出现在英国和法国，而不是意大利或日本。然而，为什么某事没有发生的问题可能很困难。除了在第一章中，我对他们几乎没有什么要说的。3、故事开始的地方。
在这里包括一个简短的 ad hominem ipsum 可能是有用的，并在本书的起源中提供这些因素的说明性示例。一段“有意识的”历史会让听起来好像任何好奇的人，至少在特定的文化中，都应该在同一个地方结束。无论在我看来多么奇怪，在我自己的情况下显然远非如此。识别一些额外的影响、条件和支持也为一些适当的确认提供了机会。其中一些是“无意识的”，只是因为我当时没有意识到它们的作用，它们的相关性是一般性的，而不是特定于本书的内容。
当然，按时间顺序，第一个影响是我的父母。我承认，对作者父母的承认总是让我觉得幼稚和陈词滥调。确实，我的父母不是知识分子，对这本书没有直接贡献。但随着时间的推移，我越来越清楚我一直认为是什么非凡的支持是理所当然的。我的母亲，在更有利的情况下，她的智慧会让她在这个方向上走得更远，而不是成为一名出色的速记员，她有一种独立的倾向，这也许在我身上找到了最大的表达机会。我仍然惊叹于我的父亲，拥有十年级的教育和坚定的传统观点，竟然能够如此支持我成为什么样的人。对于一个主要兴趣是打猎、足球、和拳击。当我看到许多人似乎很难持有与周围大多数人根本不同的观点时，我想我可能知道其中一个原因。
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