Two different questions
Political research often asks whether an apparent difference reflects a pattern rather than random variation in the data. A study might compare civic participation among groups or examine whether a change in election administration is associated with a change in turnout. Statistical significance helps researchers address a narrow question about the evidence for a pattern. Practical importance asks a different question: How large is the difference, and would it matter to people, institutions or public decisions?
These questions can have different answers. A small difference measured with great precision may be statistically significant but barely noticeable in practice. A potentially consequential difference measured with limited data may fail to meet a conventional significance threshold. Neither outcome, by itself, tells readers what policy to favor. It tells them where to look next.
What a significance test does—and does not—say
Many studies report a p-value. Under a specified statistical model, a p-value describes how unusual the observed result, or a more extreme one, would be if a stated null hypothesis were true. The null hypothesis often says there is no difference or no association. Researchers commonly compare the p-value with a threshold chosen before analysis. Passing that threshold is what a paper typically calls statistically significant.
A p-value is not the probability that the null hypothesis is true. Nor does statistical significance mean a finding is large, important or certain to recur. The calculation depends on the study’s assumptions and methods. It can also be affected by sample size: with enough observations, a very small difference may clear a significance threshold. Conversely, a study with few observations may be too imprecise to distinguish a meaningful difference from ordinary variation.
Look at size, scale and uncertainty
To judge practical importance, start with the effect size: the estimated magnitude of a difference or association. Ask what units the study uses. A change measured in percentage points is not the same as a percentage change, and a difference in survey responses is not automatically a difference in real-world behavior. Consider the baseline as well. The same-sized change can have different implications depending on how common the outcome already is.
Next, examine the uncertainty around the estimate, often shown as a confidence interval. An interval can help readers see whether the data are compatible with a range of small and large effects. It does not provide a simple guarantee about where the true effect lies. If the interval includes outcomes that would lead to very different practical judgments, the study may not yet settle the question, even if its headline emphasizes a single estimate.
Study design matters as much as a threshold
A precise estimate can still answer the wrong question. For example, an association between following political news and voting does not, on its own, show that news exposure caused someone to vote. People who already intend to participate may also seek out more news. Researchers use study design and analysis to address such alternative explanations, but no p-value can substitute for evaluating those choices.
Readers should ask whom the study includes, how key concepts were measured and whether the comparison groups are reasonably alike. They should also note whether researchers examined many outcomes or subgroups. When numerous tests are run, some results may pass a significance threshold by chance. A finding is easier to interpret when the question and analysis were specified in advance, the methods are transparent and other evidence points in a similar direction.
A practical reading checklist
When a headline calls a political finding significant, translate it into a few questions. What difference was estimated, and in what units? How wide is the uncertainty interval? Does the study support a causal claim or only an association? Would the estimated change matter for the particular decision under discussion? Finally, are there costs, trade-offs or implementation barriers that the statistical result cannot measure?
The last question depends on context. A small average change might matter across a large population, while a larger change confined to a narrow setting might not apply elsewhere. Reasonable readers can also disagree about what counts as important. Statistical methods help clarify what the data show and how uncertain the estimate is; they cannot choose civic priorities. Reading political research well means keeping the strength of the evidence separate from the significance of the decision.
