<strong>
 Preregistration Distinguishes Between Exploratory and Confirmatory Research?
</strong>
Previous justifications for preregistration have focused on the distinction between “exploratory” and “confirmatory” research. However, as I discuss in this recent presentation, this distinction faces unresolved questions.

Mark Rubin

### Preregistration Distinguishes Between Exploratory and Confirmatory Research?

Previous justifications for preregistration have focused on the
distinction between "exploratory" and "confirmatory" research. However,
as I discuss in this [recent
presentation](https://www.youtube.com/watch?v=Gw_vn71esmA), this
distinction faces unresolved questions. For example, the distinction
does not appear to have a formal definition in either statistical theory
or the philosophy of science. In addition, critics have questioned
related concerns about the "double use" of data and "circular reasoning"
(Devezer et al., 2021; Rubin, 2020, 2022; Rubin & Donkin, 2022; Szollosi
& Donkin, 2021; see also Mayo, 1996, pp. 137, 271-275; Mayo, 2018, p.
319).

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### Preregistration Improves the Transparent Evaluation of Severity

Lakens and colleagues provide a more coherent justification for
preregistration based on Mayo's (1996, 2018) error statistical approach
(Lakens, 2019, 2024; Lakens et al., 2024; see also Vize et al., 2024).
Specifically, Lakens (2019) argues that "preregistration has the goal to
allow others to transparently evaluate the capacity of a test to falsify
a prediction, or the severity of a test" (p. 221).

A hypothesis passes a severe test when there is a high probability that
it would not have passed, or passed so well, if it was false (Mayo,
1996, 2018). A test procedure's error probabilities play an important
role in evaluating severity. In particular, "pre-data, the choices for
the type I and II errors reflect the goal of ensuring the test is
capable of licensing given inferences severely" (Mayo & Spanos, 2006, p.
350). For example, a test procedure with a nominal pre-data Type I error
rate of α = 0.05 is capable of licensing specific inferences with a
minimum "worst case" severity of 0.95 (i.e., 1 -- α; Mayo, 1996, p.
399).

Importantly, "biasing selection effects" in the experimental testing
context (e.g., *p*-hacking) can lower the capability of a test procedure
to license inferences severely by increasing the error probability with
which the procedure passes hypotheses. From this error statistical
perspective, preregistration allows a more transparent evaluation of the
capability of a test procedure to perform severe tests. In particular,
preregistration reveals a researcher's *planned* hypotheses, methods,
and analyses and enables a comparison with their *reported* hypotheses,
methods, and analyses in order to identify any biasing selection effects
in the experimental testing context that may increase the test
procedure's error probabilities and lower its capability for severe
tests.

### What Type of Severity?

Mayo's (1996, 2018) error statistical conceptualization of severity is
not the only one out there! Other types of severity have been proposed
by Bandyopadhyay and Brittan (2006), Hellman (1997, p. 198), Hitchcock
and Sober (2004, pp. 23-25), Horwich (1982, p. 105), Lakatos (1968, p.
382), Laudan (1997, p. 314), Popper (1962, 1983), and van Dongen et al.
(2023). Furthermore, preregistration may not facilitate the transparent
evaluation of these other types of severity. In my article, I illustrate
this point by showing that, although preregistration can facilitate the
transparent evaluation of Mayoian severity, it does not improve the
transparent evaluation of Popperian severity.

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:::

I show that a valid measurement of Popperian severity can be made using
a potentially *p*-hacked result, a potentially HARKed hypothesis, and
potentially biased background knowledge. In addition, I show that
Popper's \"requirement of sincerity\" can be transparently evaluated
during a public critical rational discussion among scientists.
Preregistration does not facilitate a transparent evaluation in either
case because neither evaluation requires knowledge of the researcher's
planned approach or unreported biasing selection effects.

### Preregistration When Deviations are Allowed

I also argue that a preregistered test procedure that allows deviations
does not provide a more transparent evaluation of Mayoian severity than
a non-preregistered procedure. In particular, I consider deviations that
are intended to maintain or increase the validity of a test procedure in
light of unexpected issues that arise in particular samples of data
(e.g., a violation of the assumption of homogeneity). I argue that a
test procedure that allows these sample-based validity-enhancing
deviations in its implementation will suffer an unknown inflation of its
Type I error rate due to the [forking paths
problem](https://sites.google.com/site/markrubinsocialpsychresearch/replication-crisis/the-garden-of-forking-paths?authuser=0)
(Gelman & Loken, 2013, 2014). Consequently, the test procedure will have
an unknown reduction of its capability to license inferences with
Mayoian severity.

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I conclude that preregistration does not improve the transparent
evaluation of severity in Popper's philosophy of science or when
deviations are allowed.

### The Article

Rubin, M. (2024). Preregistration does not improve the transparent
evaluation of severity in Popper's philosophy of science or when
deviations are allowed. *arXiv.*
<https://doi.org/10.48550/arXiv.2408.12347>

### References

Bandyopadhyay, P. S., & Brittan, G. G. (2006). Acceptibility, evidence,
and severity. *Synthese, 148*, 259-293.
<https://doi.org/10.1007/s11229-004-6222-6>

Gelman, A., & Loken, E. (2013). *The garden of forking paths: Why
multiple comparisons can be a problem, even when there is no "fishing
expedition" or "p-hacking" and the research hypothesis was posited ahead
of time.* Department of Statistics, Columbia University. Retrieved from
<http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf> 

Gelman, A., & Loken, E. (2014). The statistical crisis in science.
*American Scientist, 102*, 460.
<http://dx.doi.org/10.1511/2014.111.460> 

Devezer, B., Navarro, D. J., Vandekerckhove, J., & Ozge Buzbas, E.
(2021). The case for formal methodology in scientific reform. *Royal
Society Open Science, 8*(3). <https://doi.org/10.1098/rsos.200805>

Hellman, G. (1997). Bayes and beyond. *Philosophy of Science, 64,*
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Hitchcock, C., & Sober, E. (2004). Prediction versus accommodation and
the risk of overfitting. *British Journal for the Philosophy of
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Horwich, P. (1982). *Probability and evidence.* Cambridge University
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Lakatos, I. (1968). Changes in the problem of inductive logic. *Studies
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[https://doi.org/10.1016/S0049-237X(08)71048-6](https://doi.org/10.1016/S0049-237X%2808%2971048-6)

Lakens, D. (2019). The value of preregistration for psychological
science: A conceptual analysis. *Japanese Psychological Review, 62*(3),
221--230. <https://doi.org/10.24602/sjpr.62.3_221>

Lakens, D. (2024). When and how to deviate from a preregistration.
*Collabra: Psychology*, 10(1): 117094.
<https://doi.org/10.1525/collabra.117094>

Lakens, D., Mesquida, C., Rasti, S., & Ditroilo, M. (2024). The benefits
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Laudan, L. (1997). How about bust? Factoring explanatory power back into
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<https://doi.org/10.1086/392553>

Mayo, D. G. (1996). *Error and the growth of experimental knowledge*.
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Mayo, D. G. (2018). *Statistical inference as severe testing: How to get
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Mayo, D. G., & Spanos, A. (2006). Severe testing as a basic concept in a
Neyman--Pearson philosophy of induction. *The British Journal for the
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<https://doi.org/10.1093/bjps/axl003>

Popper, K. R. (1962). *Conjectures and refutations: The growth of
scientific knowledge.* Routledge.

Popper, K. R. (1983). *Realism and the aim of science: From the
postscript to the logic of scientific discovery.* Routledge.

Rubin, M. (2020). Does preregistration improve the credibility of
research findings? *The Quantitative Methods for Psychology, 16*(4),
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Rubin, M. (2022). The costs of HARKing. *British Journal for the
Philosophy of Science, 73*(2), 535-560.
*[](https://doi.org/10.1093/bjps/axz050)*
<https://doi.org/10.1093/bjps/axz050>

Rubin, M. (2024). Inconsistent multiple testing corrections: The fallacy
of using family-based error rates to make inferences about individual
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<https://doi.org/10.1016/j.metip.2024.100140>

Rubin, M., & Donkin, C. (2022). Exploratory hypothesis tests can be more
compelling than confirmatory hypothesis tests. *Philosophical
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<https://doi.org/10.1080/09515089.2022.2113771>

Szollosi, A., & Donkin, C. (2021). Arrested theory development: The
misguided distinction between exploratory and confirmatory research.
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van Dongen, N., Sprenger, J., & Wagenmakers, E. J. (2023). A Bayesian
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<https://doi.org/10.3758/s13423-022-02069-1>

Vize, C., Lynam, D., Miller, J., & Phillips, N. L. (2024, May 28). On
the use and misuses of preregistration: A reply to Klonsky (2024).
*PsyArXiv*. <https://doi.org/10.31234/osf.io/g7dn2>

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Acceptibility, Evidence, and Severity

The Statistical Crisis in Science

The case for formal methodology in scientific reform

Bayes and Beyond

Prediction Versus Accommodation and the Risk of Overfitting

Changes in the Problem of Inductive Logic

When and How to Deviate From a Preregistration

The benefits of preregistration and Registered Reports

How About Bust? Factoring Explanatory Power Back into Theory Evaluation

Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction

Does preregistration improve the credibility of research findings?

The Costs of HARKing

Inconsistent multiple testing corrections: The fallacy of using family-based error rates to make inferences about individual hypotheses

Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

A Bayesian perspective on severity: risky predictions and specific hypotheses

On the Use and Misuses of Preregistration: A Reply to Klonsky (2024)

Preregistration does not improve the transparent evaluation of severity in Popper’s philosophy of science or when deviations are allowed

Preregistration, Severity, and Deviations

Messages de Rogue Scholar

Preregistration, Severity, and Deviations