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Robust misinterpretation of confidence intervals
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TLDR
Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs, which suggests that many researchers do not know the correct interpretation of a CI.Abstract:
Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students—all in the field of psychology—were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers’ performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.read more
Citations
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Global evidence of extreme intuitive moral prejudice against atheists
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References
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The earth is round (p < .05)
TL;DR: The authors reviewed the problems with null hypothesis significance testing, including near universal misinterpretation of p as the probability that H is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects H₀ one thereby affirms the theory that led to the test.
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The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis
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TL;DR: The problem of post-experiment power calculation is discussed in this paper. But, the problem is extensive and present arguments to demonstrate the flaw in the logic, which is fundamentally flawed.
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TL;DR: The book provides stronger standards for maintaining the participant confidentiality and for reducing bias in language describing participants and suggests that researchers avoid the use of derogatory language such as using “minority” for “non-white” populations.
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Introduction to Probability and Statistics
TL;DR: The twelve edition of the Introduction to Probability and Statistics (INTRODUCTION TO PROBABILITY and STATISTICS) as discussed by the authors has been used by hundreds of thousands of students since its first edition.