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Publication Bias.

Open a file drawer of studies hidden by a significance-only publication rule.

Interactive experimentintuitiveField note ·
INTERACTIVE EXPERIMENT / P22

Publication Bias

Open a file drawer of studies hidden by a significance-only publication rule.

TEST THE MECHANISM

One hundred independent studies estimate a true mean of zero with known standard deviation one and sample size 25. A selective journal publishes only |Z| > 1.96.

????-0.41??????0.40??????????-0.48??????????????????????????????????????-0.57?????0.57????????????????????????????????
Visible studies
5
Visible mean estimate
-0.099
True generating mean
0
Selection rule
Two-sided |Z| > 1.96

Two-sided selection exaggerates absolute effects even when positive and negative selected effects average close to zero. This toy does not estimate how prevalent publication bias is in a field.

Two-sided selection exaggerates absolute estimates without necessarily producing a large signed average. The simulation does not measure the prevalence of bias in any actual field.

THE SHORT VERSION

Publication Bias, explained.

Publication bias occurs when availability of research results depends on their findings, distorting the evidence that readers can access.

01 / THE MECHANISM

Why it happens

One hundred independent known-variance Normal studies estimate a true zero mean. The journal reveals only absolute z scores above 1.96; opening the drawer reveals all estimates and the selection rule.

Publication bias occurs when availability of research results depends on their findings, distorting the evidence that readers can access.

Read the result

Visible signed means can remain near zero under symmetric two-sided selection. Inspect which absolute estimates became visible, not only their average sign.

02 / FOLLOW IT THROUGH

A worked example

Open the drawer behind significant results

  1. Inspect the selective journal’s visible estimates from the true-zero study generator.

  2. Open the file drawer to reveal every one of the hundred synthetic studies.

  3. Compare the selected extreme estimates with the complete distribution, preserving the same realised batch.

OPTIONAL DEEPER DETAILGo deeper: inside the model

Inside this model

One hundred independent known-variance Normal studies estimate a true zero mean. The journal reveals only absolute z scores above 1.96; opening the drawer reveals all estimates and the selection rule.

03 / BEYOND THE EXPERIMENT

Where this idea is useful

A practical use

Ask what unpublished studies would change an apparent evidence base before treating visible findings as a complete sample.

CHECK YOUR INTUITION

A common misconception

THE TEMPTING CONCLUSION

“Publication selection must always create a positive average effect.”

THE MORE USEFUL DISTINCTION

Two-sided selection can retain both positive and negative extremes. Exaggerated absolute magnitude and a biased signed average are different outcomes.

What this explanation leaves out

  • Two-sided selection exaggerates absolute estimates without necessarily producing a large signed average. The simulation does not measure the prevalence of bias in any actual field.
ONE MORE QUESTION

Is the hidden-study count a statistic about real journals?

No. The significance-only rule is stipulated for a teaching batch, not estimated from publication practices in any field.

TAKE THE IDEA WITH YOU

What result-selection rule separates the evidence you see from the evidence collected?

Associated thinkers

Associated withRobert Rosenthal ↗

Associations marked provisional are awaiting source review.

Further reading

Explore the original research or the teaching reference behind this experiment.