Mark Rubin

In this
[paper](https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1037%2Fgpr0000135&sa=D&sntz=1&usg=AFQjCNGPXhRTKBRRFOs9Cvs9a3pW4QQRPQ)
(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. For example,
a researcher may decide whether or not to drop an item from a scale
based on how it affects the scale's Cronbach alpha coefficient *within
the current sample.* This *sample-contingent decision rule* causes a
forking path in the analysis protocol because, during the course of
repeated sampling from the same population, some samples may yield an
acceptable Cronbach alpha value, leading to the retention of the item in
the scale, and some samples may yield an unacceptable Cronbach alpha,
leading to the deletion of the item.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*-41uiff_DcjfnK_X6y8JKQ.png" />
<figcaption>Source: <a
href="https://doi.org/10.1037/gpr0000135">https://doi.org/10.1037/gpr0000135</a></figcaption>
</figure>

Following the garden of forking paths would lead to multiple tests of
the same hypothesis during a long run of exact replications of the
analysis protocol (e.g., one test that included the item in the scale
and one test that excluded the item from the scale). Consequently, the
forking paths problem represents a special case of the multiple testing
problem. Forking paths increase the familywise Type I error rate for a
hypothesis by taking into account not only the test that was actually
conducted, but also the test that could have been conducted had a
different sample been drawn.

In my paper, I consider four potential solutions to the forking paths
problem: (a) preregistration, (b) sensitivity (robustness, multiverse)
analyses, (c) adjusting the alpha level, and (d) abandoning the
Neyman-Pearson approach. I conclude that preregistration and sensitivity
analyses are ineffective solutions, but that adjusting the alpha level
and/or abandoning the Neyman-Pearson approach are effective solutions.
In particular, the alpha level can be adjusted to take into account the
number of forking paths that are involved in testing the relevant
hypothesis (e.g., α/2 for one forking path, α/4 for two forking paths,
etc.). In addition, statistical inference approaches that do not rely on
the concept of repeated sampling from the same population are not
susceptible to the forking paths problem. Hence, the Fisherian and
Bayesian approaches to hypothesis testing do not suffer from the forking
paths problem because they both condition their probability statements
on the test that was actually conducted and the sample that was
actually drawn.

For further information, please see:

Rubin, M. (2017). An evaluation of four solutions to the forking paths
problem: Adjusted alpha, preregistration, sensitivity analyses, and
abandoning the Neyman-Pearson approach. *Review of General Psychology,
21*(4)*,* 321--329.
[https://doi.org/10.1037/gpr0000135](https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1037%2Fgpr0000135&sa=D&sntz=1&usg=AFQjCNGPXhRTKBRRFOs9Cvs9a3pW4QQRPQ)
\*[Self-archived
version](https://drive.google.com/file/d/1wjOrFqqjw5aaISyKZANuRhXpjPkAPl1K/view?usp=sharing)\*

For more of my work in this area, please see:
<https://sites.google.com/site/markrubinsocialpsychresearch/replication-crisis>

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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.

Messaggi di Rogue Scholar

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