Statistical significance

Statistical significance describes whether an observed difference or association is large enough, relative to sampling variation, that it is unlikely to have appeared by chance under the null hypothesis. It is expressed as a p-value: the probability of seeing a result at least as extreme as the one observed if no real effect existed. A threshold of p < 0.05 is conventional in biomedical research.

That threshold means fewer than a 5-in-100 chance that chance alone produced the pattern. It is a gatekeeping concept in published research: results that cross it are described as statistically significant; those that do not are called non-significant or null. This language shapes how findings are reported across pharmacology, clinical trials, and epidemiology — and how they are frequently misread.

What this design can establish

A statistically significant result establishes that an observed association or difference is unlikely to be a product of sampling error alone, given the study's design and the test applied. This is a meaningful but narrow claim: the signal is probably real in the sense that it is not simply noise in the data.

It supports claims about direction — whether a drug performs better than placebo, whether two groups differ on a measured outcome — and about internal consistency within a single study. When a trial is adequately powered, a significant result also reflects precision: the sample was large enough for the pattern to emerge as detectable.

What it cannot

Statistical significance says nothing about how large or meaningful an effect is. A result can be highly significant — p < 0.001 — and describe an effect too small to matter in practice. Conversely, a non-significant result does not mean no effect exists; it often means the study was too small to detect one. This misreading runs in both directions and is the most consequential one a reader is likely to make.

Significance cannot establish causation. A significant association may reflect confounding, reverse causation, or coincidence across many tested hypotheses. In pharmacology specifically, it cannot confirm that a finding is reproducible — replication failures are common, and first-study effect estimates are frequently inflated in underpowered fields.

Publication bias compounds this. Studies with significant findings are more likely to be published, so the evidence available to readers skews toward positives. A single significant result, absent replication, tells you that a pattern appeared once in one sample — not that it reflects a reliable underlying effect.