## 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.

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## Acknowledgements

This work was supported by the starting grant “Bayes or Bust” awarded by the European Research Council, and by National Science Foundation Grants BCS-1240359 and SES-102408.

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## Appendices

### Appendix 1 Questionnaire on p-values (Gigerenzer, 2004)

(The scenario and the table are reproduced verbatim from Gigerenzer [2004, p. 594].)

Suppose you have a treatment that you suspect may alter performance on a certain task. You compare the means of your control and experimental groups (say 20 subjects in each sample). Further, suppose you use a simple independent means *t*-test and your result is significant (*t* = 2.7, d.f. = 18, *p* = 0.01). Please mark each of the statements below as “true” or “false.” “False” means that the statement does not follow logically from the above premises. Also note that several or none of the statements may be correct (between the population means).

1. You have absolutely disproved the null hypothesis (that is, there is no difference between the population means).

[] true/false []

2. You have found the probability of the null hypothesis being true.

[] true/false []

3. You have absolutely proved your experimental hypothesis (that there is a difference between the population means).

[] true/false []

4. You can deduce the probability of the experimental hypothesis being true.

[] true/false []

5.You know, if you decide to reject the null hypothesis, the probability that you are making the wrong decision.

[] true/false []

6. You have a reliable experimental finding in the sense that if, hypothetically, the experiment were repeated a great number of times, you would obtain a significant result on 99 % of occasions.

[] true/false []

### Appendix 2 Questionnaire on confidence intervals

(The questionnaires for the students were in Dutch, and the researchers could choose between an English and a Dutch version.)

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Hoekstra, R., Morey, R.D., Rouder, J.N. *et al.* Robust misinterpretation of confidence intervals.
*Psychon Bull Rev* **21, **1157–1164 (2014). https://doi.org/10.3758/s13423-013-0572-3

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### Keywords

- Confidence intervals
- Significance testing
- Inference