Modeling Social Data, Lecture 6: Reproducibility and replication, Part 2

Modeling Social Data, Lecture 6: Reproducibility and replication, Part 2

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Jake Hofman

March 01, 2019
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    Reproducibility, replication, etc., Part 2 APAM E4990 Modeling Social Data

    Jake Hofman Columbia University March 1, 2019 Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 1 / 18
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    Questions How should one evaluate research results? • Was the

    research done and reported honestly / correctly? • Is the result “real” or an artifact of the data / analysis? • Will it hold up over time? • How robust is the result to small changes? • How important / useful is the finding? Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 2 / 18
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    Replicability Will the result hold up with new data but

    the same analysis? • It’s easy to be fooled by randomness • Noise can dominate signal in small datasets • Asking too many questions of the data can lead to overfitting Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 3 / 18
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    Crisis Believe about half of what you read Jake Hofman

    (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 4 / 18
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    Crisis insufficient specification of the conditions nec- essary or sufficient

    to obtain the results. Direct replication is the attempt to recreate the con- ditions believed sufficient for obtaining a pre- The mean effect size (r) of the replication ef- fects (Mr = 0.197, SD = 0.257) was half the mag- nitude of the mean effect size of the original effects (Mr = 0.403, SD = 0.188), representing a the original effect sizes. Moreove evidence is consistent with the co variation in the strength of in (such as original P value) was m of replication success than var characteristics of the teams co research (such as experience a The latter factors certainly can lication success, but they did no so here. Reproducibility is not well un cause the incentives for individ prioritize novelty over replica tion is the engine of discovery a a productive, effective scientif However, innovative ideas beco fast. Journal reviewers and edi miss a new test of a publishe original. The claim that “we alrea belies the uncertainty of scient Innovation points out paths tha replication points out paths th progress relies on both. Replic crease certainty when findings a and promote innovation when This project provides accumula for many findings in psycholog and suggests that there is still do to verify whether we know w we know. ▪ SCIENCE sciencemag.org 28 AUGUST 2015 • VOL 349 ISS The list of author affiliations is available in the *Corresponding author. E-mail: nosek@virg Cite this article as Open Science Collabora aac4716 (2015). DOI: 10.1126/science.aac4 Original study effect size versus replication effect size (correlation coefficients). Diagonal line represents replication effect size equal to original effect size. Dotted line represents replication effect size of 0. Points below the dotted line were effects in the opposite direction of the original. Density plots are separated by significant (blue) and nonsignificant (red) effects. Believe about half of what you read Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 5 / 18
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    Statistics! Hypothesis testing? P-values? Statistical significance? Confidence intervals?? Effect sizes???

    xkcd.com/892 Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 6 / 18
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    Misunderstandings ESSAY Statistical tests, P values, confidence intervals, and power:

    a guide to misinterpretations Sander Greenland1 • Stephen J. Senn2 • Kenneth J. Rothman3 • John B. Carlin4 • Charles Poole5 • Steven N. Goodman6 • Douglas G. Altman7 Received: 9 April 2016 / Accepted: 9 April 2016 / Published online: 21 May 2016 Ó The Author(s) 2016. This article is published with open access at Springerlink.com Abstract Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instruc- tors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting. Editor’s note This article has been published online as supplementary material with an article of Wasserstein RL, Lazar NA. The ASA’s statement on p-values: context, process and purpose. The American Statistician 2016. Albert Hofman, Editor-in-Chief EJE. 3 Eur J Epidemiol (2016) 31:337–350 DOI 10.1007/s10654-016-0149-3 Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 7 / 18
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    Quiz • You do 1,000 experiments for 1,000 different research

    questions • Only 30% of these experiments investigate real effects • You set your significance level α to 5% • You use a small sample size such that your power 1 − β is 35% • Given that one of these experiments shows statistical significance, what’s the probability that it’s a real effect? Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 8 / 18
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    Underpowered studies Essay Open access, freely available online factors that

    infl uence this problem and some corollaries thereof. Modeling the Framework for False Positive Findings Several methodologists have pointed out [9–11] that the high rate of nonreplication (lack of confi rmation) of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research fi ndings solely on the basis of a single study assessed by formal statistical signifi cance, typically for a p-value less than 0.05. Research is not most appropriately represented and summarized by p-values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p-values. Research fi ndings are defi ned here as any relationship reaching formal statistical signifi cance, e.g., is characteristic of the fi eld and can vary a lot depending on whether the fi eld targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fi elds where either there is only one true relationship (among many that can be hypothesized) or the power is similar to fi nd any of the several existing true relationships. The pre-study probability of a relationship being true is R⁄(R + 1). The probability of a study fi nding a true relationship refl ects the power 1 − β (one minus the Type II error rate). The probability of claiming a relationship when none truly exists refl ects the Type I error rate, α. Assuming that c relationships are being probed in the fi eld, the expected values of the 2 × 2 table are given in Table 1. After a research fi nding has been claimed based on achieving formal statistical signifi cance, the post-study probability that it is true is the positive predictive value, PPV. The PPV is also the complementary Why Most Published Research Findings Are False John P. A. Ioannidis Summary There is increasing concern that most current published research fi ndings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientifi c fi eld. In this framework, a research fi nding is less likely to be true when the studies conducted in a fi eld are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater fl exibility in designs, defi nitions, outcomes, and analytical modes; when there is greater fi nancial and other interest and prejudice; and when more teams are involved in a scientifi c fi eld in chase of statistical signifi cance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientifi c fi elds, claimed research fi ndings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the It can be proven that most claimed research fi ndings are false. Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 11 / 18
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    P-hacking Psychological Science 22(11) 1359 –1366 © The Author(s) 2011

    Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797611417632 http://pss.sagepub.com Our job as scientists is to discover truths about the world. We generate hypotheses, collect data, and examine whether or not Which control variables should be considered? Should spe- cific measures be combined or transformed or both? False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant Joseph P. Simmons1, Leif D. Nelson2, and Uri Simonsohn1 1The Wharton School, University of Pennsylvania, and 2Haas School of Business, University of California, Berkeley Abstract In this article, we accomplish two things. First, we show that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process. Keywords methodology, motivated reasoning, publication, disclosure Received 3/17/11; Revision accepted 5/23/11 General Article Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 12 / 18
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    Publication / citation bias While only 50% of FDA-registered studies

    on antidepressants find positive results, but 95% of publications report positive findings. bit.ly/depressionspin Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 16 / 18
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    So, what should you do? • Read the literature •

    Formulate your study • Run a simple pilot • Analyze the results • Revise your study (null != nil) • Do a power calculation • Pre-register your plans • Run your study • Create a reproducible report • Think critically about results • Disclose everything you did Jake Hofman (Columbia University) Reproducibility, replication, etc., Part 2 March 1, 2019 18 / 18