In psychological research, scientists often calculate ‘p-values’ to determine whether their hypotheses can be supported. P-values provide an insight into whether we can expect identified relations in a sample, for instance, whether men and women differ in how much self-control they have, or whether age is a predictor of wellbeing, are likely to also manifest or exist similarly in the broader population of interest who can’t be directly measured because it would be unfeasible to do so. Importantly, researchers often acknowledge in advance that they will only claim support for their hypotheses if the calculated p-value is below a threshold they set before analyzing their data; this threshold (which is usually set at .05) should generally be small enough that the researcher, if they were to (hypothetically) collect 100 separate samples to test the focal effects, would very infrequently (with a .05 threshold, only 5% of the time) find an effect when one doesn’t actually exist in the population. In other words, the p-value gives the researcher some confidence that they are not interpreting error variance as the effect. If the researcher finds the calculated p-value for the effect to be above their pre-set threshold, the ‘null hypothesis’ that there is no effect should not be rejected. In this instance the researcher should simply withhold judgement on any claim for an effect.