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,
2020](https://drive.google.com/file/d/13cWuP8ilmQWMSORmzr6pLf3mJtn2C0jx/view?usp=sharing)),
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. If they don't know what counts as the same or
equivalent population, then they can't specify a procedure that would be
able to repeatedly sample from this population, rather than from other
non-equivalent populations, and without this specification the
Neyman-Pearson long run error rates become meaningless.

I argue that, by definition, researchers do not know for sure what are
the relevant and irrelevant features of their current populations. For
example, in a psychology study, is the population "1st year
undergraduate psychology students" or, more narrowly, "Australian 1st
year undergraduate psychology students" or, more broadly, "psychology
undergraduate students" or, even more broadly, "young people," etc.?
Researchers can make educated guesses about the relevant and irrelevant
aspects of their population. However, they must concede that those
guesses may be wrong. Consequently, if a researcher imagines a long run
of repeated sampling, then they must imagine that they would make
incorrect decisions about their null hypothesis due to not only Type I
errors and Type II errors, but also Type III errors --- errors caused by
accidentally sampling from populations that are substantively different
to their underspecified alternative and null populations.

To be clear, the Neyman-Pearson approach *does* consider Type III
errors. However, it considers them *outside* of each long run of
repeated sampling. It does not allow Type III errors to occur *inside* a
long run of repeated sampling, where the sampling must always be from a
correctly specified family of "admissible" populations (Neyman, 1977, p.
106; Neyman & Pearson, 1933, p. 294). In my paper, I argue that
researchers are unable to imagine a long run of repeated sampling from
the same or equivalent populations as their current population because
they are unclear about the relevant and irrelevant characteristics of
their current population. Consequently, they are unable to rule out Type
III errors within their imagined long run.

Following Fisher, I contrast scientific researchers with quality
controllers in industrial production settings. Unlike researchers,
quality controllers have clear knowledge about the relevant and
irrelevant characteristics of their populations. For example, they are
given a clear and unequivocal definition of Batch 57 on a production
line, and they don't consider re-conceptualizing Batch 57 as including
or excluding other features. They also know which aspects of their
testing procedure are relevant and irrelevant, and they are provided
with precise quality control standards that allow them to know, for
sure, their smallest effect size of interest. Consequently, the
Neyman-Pearson approach is suitable for quality controllers because
quality controllers can imagine a testing process that repeatedly draws
random samples from the same population over and over again. In
contrast, the Neyman-Pearson approach is not appropriate in scientific
investigations because researchers do not have a clear understanding of
the relevant and irrelevant aspects of their populations, their tests,
or the smallest effect size that represents their population. Indeed,
they are "researchers" because they are "researching" these things.
Hence, it is researchers' self-declared ignorance and doubt about the
nature of their populations that renders Neyman-Pearson long run error
rates scientifically meaningless.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*uUO4ovE7vJWkr5NObePNBw.jpeg" />
<figcaption>Source: <a
href="https://dissolve.com/stock-photo/Quality-control-workers-digital-tablet-examining-blocks-royalty-free-image/101-D1230-1-512">https://dissolve.com/stock-photo/Quality-control-workers-digital-tablet-examining-blocks-royalty-free-image/101-D1230-1-512</a></figcaption>
</figure>

For further information, please see:

Rubin, M. (2020). "Repeated sampling from the same population?" A
critique of Neyman and Pearson's responses to Fisher. *European Journal
for Philosophy of Science, 10*, Article 42, 1--15*.*
[https://doi.org/10.1007/s13194-020-00309-6](https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1007%2Fs13194-020-00309-6&sa=D&sntz=1&usg=AFQjCNHY2NhGe0PmkpF4-n_IgbhMNd4z3A)
\*[Publisher's open access view only
version](https://www.google.com/url?q=https%3A%2F%2Frdcu.be%2Fb7K0s&sa=D&sntz=1&usg=AFQjCNGf6S0omDOBOhaS48krOpG8TgGDQA)\*
\*[Author's
version](https://drive.google.com/file/d/13cWuP8ilmQWMSORmzr6pLf3mJtn2C0jx/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/what-is-a-type-i-error).

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