Mark Rubin

In this paper (Rubin, 2022), I make two related points: (1) researchers should halve two-sided

 p

values if they wish to use them to make directional claims, and (2) researchers should

 not

halve their alpha level if they’re using two one-sided tests to test two directional null hypotheses.

One-Sided Significance Tests

Researchers often distinguish between:  (1) Exploratory hypothesis tests — unplanned tests of post hoc hypotheses that may be based on the current results, and  (2) Confirmatory hypothesis tests — planned tests of a priori hypotheses that are independent from the current results  This distinction is supposed to be useful because exploratory results are assumed to be more “tentative” and “open to bias” than confirmatory results.

Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

In this paper (Rubin, 2017), I consider Gelman and Loken’s (2013, 2014) garden of forking paths problem. Forking paths occur when researchers decide which analyses to perform based on information from their sample.

An Evaluation of Four Solutions to the Garden of Forking Paths Problem

The Costs of HARKing: Does it Matter if Researchers Engage in Undisclosed Hypothesizing After the Results are Known? While no-one’s looking, a Texas sharpshooter fires his gun at a barn wall, walks up to his bullet holes, and paints targets around them. When his friends arrive, he points at the targets and claims he’s a good shot.

The Costs of HARKing: Does it Matter if Researchers Engage in Undisclosed Hypothesizing After the…

In this new article, I consider questionable research practices in the field of metascience. A

 questionable metascience practice

(QMP) is a research practice, assumption, or perspective that’s been questioned by several commentators as being potentially problematic for metascience and/or the science reform movement. I discuss 10 QMPs that relate to criticism, replication, bias, generalization, and the characterization of science.

Questionable Metascience Practices

Preregistration entails researchers registering their planned research hypotheses, methods, and analyses in a time-stamped document before they undertake their data collection and analyses. This document is then made available with the published research report in order to allow readers to identify discrepancies between what the researchers originally planned to do and what they actually ended up doing.

Does Preregistration Improve the Interpretablity and Credibility of Research Findings?

In this new paper (Rubin, 2021), I consider when researchers should adjust their alpha level (significance threshold) during multiple testing and multiple comparisons. I consider three types of multiple testing (disjunction, conjunction, and individual), and I argue that an alpha adjustment is only required for one of these three types.

When to Adjust Alpha During Multiple Testing

In this paper (Rubin, 2021), I consider two types of Type I error probability. The

 Neyman-Pearson Type I error rate

refers to the maximum frequency of incorrectly rejecting a null hypothesis if a test was to be repeatedly reconducted on a series of different random samples that are all drawn from the exact same null population. Hence, the Neyman-Pearson Type I error rate refers to a long run of

 exact

replications.

What Type of Type I Error?

In this paper (Rubin, 2020), I consider Fisher’s criticism that the Neyman-Pearson approach to hypothesis testing relies on the assumption of “repeated sampling from the same population.” This criticism is problematic for the Neyman-Pearson approach because it implies that test users need to know, for sure, what counts as the same or equivalent population as their current population.

Repeated Sampling from the Same Population?

Stories by Mark Rubin on Medium

In this paper ([Rubin,
20](https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1007%2Fs11229-019-02433-0&sa=D&sntz=1&usg=AFQjCNEVuCuq3elOVD71HY_Ee1DN_5q1Og)21),
I consider two types of Type I error probability. The *Neyman-Pearson
Type I error rate* refers to the maximum frequency of incorrectly
rejecting a null hypothesis if a test was to be repeatedly reconducted
on a series of different random samples that are all drawn from the
exact same null population. Hence, the Neyman-Pearson Type I error rate
refers to a long run of *exact* replications. In contrast, the
*Fisherian Type I error probability* is the probability of incorrectly
rejecting a null hypothesis in relation to a hypothetical population
that reflects the relevant characteristics of the particular sample
under consideration. Hence, the Fisherian Type I error rate refers to a
one-off sample rather than a series of samples that are drawn during a
long run of exact replications.

I argue that social science deals with units of analysis (people, social
groups, and social systems) that change over time. As the Greek
philosopher Heraclitus put it: "No man ever steps in the same river
twice, for it's not the same river and he's not the same man." Rivers
and men are what Schmidt (2009, p. 92) called "irreversible units" in
that they are complex time-sensitive systems that accumulate history.
The scientific investigation of these irreversible units cannot proceed
on the assumption that exact replications are possible. Consequently,
the Neyman-Pearson Type I error rate is irrelevant in social science,
because it relies on a concept of exact replication that cannot take
place in the case of people, social groups, and social systems. Why
should social scientists be interested in an error rate for an
impossible process of resampling from the same fixed and unchanging
population?

<figure>
<img
src="https://cdn-images-1.medium.com/max/554/1*zfDlYm8FEwiXhSQ8QrnI2g.jpeg" />
<figcaption>Source: <a
href="https://images.app.goo.gl/RiEP5tPX75AVbmFy6">https://images.app.goo.gl/RiEP5tPX75AVbmFy6</a></figcaption>
</figure>

I argue that the Fisherian Type I error probability is more appropriate
in social science because it refers to one-off samples from hypothetical
populations. In this case, researchers recognise that every sample comes
from a potentially different population. Hence, researchers can apply
the Fisherian Type I error probability to each sample-specific
provisional decision that they make about rejecting the same substantive
null hypothesis in a series of *direct* replications.

I conclude that the replication crisis may be partly (not wholly) due to
researchers' unrealistic expectations about replicability based on their
consideration of the Neyman-Pearson Type I error rate across a long run
of exact replications.

For further information, please see:

Rubin, M. (2021). What type of Type I error? Contrasting the
Neyman-Pearson and Fisherian approaches in the context of exact and
direct replications. *Synthese, 198,* 5809--5834*. *.
[https://doi.org/10.1007/s11229-019-02433-0](https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1007%2Fs11229-019-02433-0&sa=D&sntz=1&usg=AFQjCNEVuCuq3elOVD71HY_Ee1DN_5q1Og)
\*[Self-archived
version](https://drive.google.com/file/d/10mEkuIElCGE5lYO-24HCYS8NKfytZBpG/view?usp=sharing)\*

I also discuss the differences between the Fisherian and Neyman-Pearson
approaches to hypothesis
testing [here](https://sites.google.com/site/markrubinsocialpsychresearch/replication-crisis/repeated-sampling).

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