The *Basic and Applied Social Psychology* (BASP) 2014 Editorial emphasized that the null hypothesis significance testing procedure (NHSTP) is invalid, and thus authors would be not required to perform it (Trafimow, 2014). However, to allow authors a grace period, the Editorial stopped short of actually banning the NHSTP. The purpose of the present Editorial is to announce that the grace period is over. From now on, BASP is banning the NHSTP.

With the banning of the NHSTP from BASP, what are the implications for authors? The following are anticipated questions and their corresponding answers.

**Question 1.** *Will manuscripts with p-values be desk rejected automatically?*

**Answer to Question 1.** No. If manuscripts pass the preliminary inspection, they will be sent out for review. But prior to publication, authors will have to remove all vestiges of the NHSTP (*p*-values, *t*-values, *F*-values, statements about “significant” differences or lack thereof, and so on).

**Question 2.** *What about other types of inferential statistics such as confidence intervals or Bayesian methods?*

**Answer to Question 2.** Confidence intervals suffer from an inverse inference problem that is not very different from that suffered by the NHSTP.

- [ ] digtk inverse inference problem

<aside> 💡 In the NHSTP, the problem is in traversing the distance from the probability of the finding, given the null hypothesis, to the probability of the null hypothesis, given the finding.

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Regarding confidence intervals, the problem is that, for example, a 95% confidence interval does not indicate that the parameter of interest has a 95% probability of being within the interval.

Rather, it means merely that if an infinite number of samples were taken and confidence intervals computed, 95% of the confidence intervals would capture the population parameter.

<aside>
💡 Analogous to how **the NHSTP fails to provide the probability of the null hypothesis, which is needed to provide a strong case for rejecting it, confidence intervals do not provide a strong case for concluding that the population parameter of interest is likely to be within the stated interval. Therefore, confidence intervals also are banned from BASP**.

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BKTK see other paper for more context:

Trafimow, 2009 - No justification for computing p values

<aside>
💡 THERE HAS BEEN A GREAT DEAL OF CONTROVERSY about the null hypothesis significance testing procedure (NHSTP) involving a large number of supporters (e.g., Abelson, 1997; Chow, 1998; Hagen, 1997; Mulaik, Raju, & Harshman, 1997) and a large number of detractors (e.g., Bakan, 1966; Cohen, 1994; Rozeboom, 1960; Schmidt, 1996; Schmidt & Hunter, 1997). **Stated briefly, NHSTP requires that the researcher propose a null hypothesis and an alternative hypothesis, collect data, and use the data to compute the probability of obtaining a finding as extreme or more extreme than the one actually obtained, given that the null hypothesis is true**. If this probability is low (e.g., p < .05), then the researcher rejects the null hypothesis in favor of the alternative hypothesis. Otherwise, the null hypothesis is not rejected.

Good definition

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<aside>
💡 Possibly, **the most compelling argument against NHSTP is that it is logically invalid. Stated simply, the fact that a rare finding, given the null hypothesis, has been obtained does not justify the conclusion that the null hypothesis is likely to be false**.

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<aside>
💡 However, this conclusion is not justified. To see why, it is useful to consider exactly what **p is: the probability of the finding (or a more extreme finding) given that the null hypothesis is true**. Or, in terms of the present example, it is the probability of obtaining the finding given that the samples of participants who were or were not given money are from the same population.

Good definition of p-value

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