For over sixty years, psychologists have been told that their studies are underpowered.
Starting with Cohen (1962) and repeated by Sedlmeier and Gigerenzer (1989), Maxwell (2004), Button et al. (2013), and countless meta-analyses, the message has been consistent: our typical studies lack the statistical sensitivity to detect true effects.

This “power failure” has been a cornerstone of the open science movement and a standard explanation for the replication crisis.

But according to a recent Psychological Methods editorial decision, that entire literature rests on a fundamental ontological error.

Apparently, there is no power failure—because, by definition, completed studies do not have power.

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The New Logic

In the journal’s interpretation (based on Pek, Hoisington-Shaw, & Wegener, 2024):

• Statistical power is a property of a test, not of a study.
• It is defined only before data are collected, for a hypothetical infinite series of replications with a fixed true effect size.
• Once data exist, the test’s long-run property no longer applies; therefore, it makes no sense to speak of a study’s power—past or present.

Hence, Cohen (1962) didn’t actually discover that psychology was underpowered.
He merely performed a few algebraic exercises about hypothetical tests.
The claim that real studies were underpowered is, under this logic, a category mistake.

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The Consequences of Redefinition

If this view is taken seriously:

• Cohen (1962, 1988), Sedlmeier & Gigerenzer (1989), Rossi (1990), Maxwell (2004), and Button et al. (2013) all committed the same “ontological error.”
• There can be no such thing as observed or average power, since power is not a property of results.
• And since no real study can ever be “underpowered,” there can be no “power failure” to explain replication failures.

Replication failures must therefore be due to something else—perhaps the planets, bad luck, or metaphysical indeterminacy.

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The Irony

Of course, the same journal publishes meta-analyses that estimate “average power” and discuss its implications for replicability. It also accepts simulations in which average power determines expected replication rates. But when that same concept is applied to actual psychological research, it suddenly becomes “ontologically incoherent.” In other words, power matters—except when it matters.

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The Bigger Picture

Sarcasm aside, this position would erase one of the few robust empirical generalizations about psychology:

that typical studies have a low probability of detecting true effects.

and that publication bias explains the 90% success rates in psychology journals (Sterling, 1959; Sterling et al., 1995; Motyl et al., 2017).

That claim—whether estimated via Cohen’s early surveys, Sedlmeier & Gigerenzer’s analyses, or modern bias-corrected methods like z-curve—has accurately predicted the replication crisis.

To declare the concept meaningless is not theoretical progress; it’s conceptual retreat.
It protects the purity of Neyman–Pearson logic at the cost of empirical relevance.
If taken literally, the new Psychological Methods stance means that “power” applies only to imaginary studies—and that real studies, by definition, can never fail.

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Closing Line

So congratulations, psychology.
After six decades of self-criticism, we can finally declare victory:
our research is not underpowered—because power no longer exists.

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