In 2011, researchers at the pharmaceutical company Bayer found that they could only replicate about 25% of published findings in their attempts to develop new drugs. A similar effort at Amgen succeeded with just 6 of 53 landmark cancer studies. These weren't sloppy experiments—they were influential papers in top journals that had shaped entire research programs.

This 'reproducibility crisis' has shaken confidence in scientific findings across psychology, medicine, economics, and beyond. But it also reveals something philosophically fascinating: reproducing an experiment is far more complicated than it sounds. When scientists fail to replicate a result, what exactly has gone wrong? The answer forces us to confront deep questions about what makes science reliable in the first place.

When we say scientists should replicate experiments, we imagine something straightforward: do the same thing again and see if you get the same result. But what counts as 'the same thing'? Every experiment involves countless details—the exact equipment used, the temperature of the room, the time of day, the population sampled, even the software version running the analysis.

Some of these details matter enormously. Others don't matter at all. The problem is that we often don't know which is which until a replication fails. A psychology study conducted on American undergraduates might not replicate with German retirees. Is that a failed replication or a new discovery about cultural differences? The answer requires judgment, not just measurement.

This creates what philosophers call the experimenter's regress. We want replications to tell us whether the original finding was real. But to know if a replication was properly conducted, we need to know which variables were relevant—and that depends on whether the original effect is real. We're caught in a circle. Determining what counts as the 'same experiment' involves irreducible judgment calls that no checklist can fully capture.

Takeaway

Replication isn't just a mechanical check—it requires understanding which features of an experiment matter, knowledge that often only emerges through repeated attempts and failure.

Much of the reproducibility crisis traces to a fundamental misunderstanding of probability. Scientists typically consider a result 'significant' if there's less than a 5% chance it would occur by random variation alone—the famous p < 0.05 threshold. But this number doesn't mean what most people think it means.

A p-value of 0.05 tells you: if there's no real effect, there's a 5% chance of seeing data this extreme. It does not tell you there's a 95% chance the effect is real. The difference matters enormously. If you test twenty hypotheses and none of them are true, you'll still get one 'significant' result on average. Run enough experiments, and false positives accumulate.

This problem compounds when researchers—consciously or not—engage in 'p-hacking': analyzing data multiple ways, adding or removing variables, or stopping data collection when significance is reached. None of these practices involve fraud, but they inflate the false positive rate dramatically. A field full of honest researchers following accepted practices can still produce a literature filled with findings that won't replicate.

Takeaway

Statistical significance was designed to control error rates across many experiments, not to measure the probability that any single finding is true—a mismatch that systematically misleads both scientists and the public.

Imagine a hundred research groups all testing the same hypothesis, which happens to be false. By chance alone, five of them will get 'significant' results. Those five submit papers; the other ninety-five file their null results away and move on. Journals publish the five positive findings. Anyone reading the literature sees overwhelming evidence for something that isn't real.

This is publication bias, and it's perhaps the most corrosive force in science. Journals prefer novel, positive results. Reviewers and editors find null results boring. Researchers face career pressure to publish exciting findings. The entire incentive structure conspires to make the published record systematically unrepresentative of what scientists actually discover.

The consequences ripple outward. Meta-analyses that combine published studies inherit their biases. Research programs chase phantom effects. Resources flow toward ideas that seemed promising only because the failures were hidden. Science's self-correcting mechanism depends on failures being visible—but the current system renders them invisible. The literature becomes a highlight reel, not a faithful record.

Takeaway

Science corrects itself through failure, but when failures go unpublished, the correction mechanism breaks down—what we see in journals may systematically misrepresent what scientists actually find.

The reproducibility crisis isn't evidence that science is broken—it's evidence that science is complicated. Replication requires judgment about what matters. Statistical tools can mislead when misunderstood. Publication incentives can distort the record. Recognizing these problems is the first step toward addressing them.

Many reforms are underway: pre-registration of hypotheses, registered reports that commit to publishing regardless of results, and open sharing of data and methods. These changes don't solve everything, but they show science doing what it does best—turning critical scrutiny on itself.