The threshold provides a preset comparison point for the p-value. When the p-value is at or below the selected alpha level, researchers label the result statistically significant because the observed data are considered statistically unusual under the null hypothesis. This label supports a decision about evidence, but it does not describe the size or practical value of the finding.
Predetermining the threshold establishes the decision rule before researchers see the analysis results. Reporting that choice improves transparency and makes it clearer how the evidence was evaluated. In psychological research, stating the alpha level in advance helps readers distinguish a planned inferential standard from a cutoff selected after viewing the data, which could otherwise complicate interpretation.
A result can meet the significance threshold without showing a large or meaningful effect. The threshold addresses whether the data provide sufficient evidence against the null hypothesis, not whether the finding matters in practice. Researchers therefore need effect size and confidence intervals to judge the magnitude and precision of an observed psychological result alongside its p-value.
The overview identifies 0.05 as a commonly used alpha level, but the important requirement is that the threshold be predetermined and reported. A comparison with 0.05 alone cannot show that a finding is practically important. Interpretation should also consider confidence intervals, effect size, sample size, and replication evidence rather than treating one conventional cutoff as a complete evaluation.
Researchers first set and report the alpha level before analysis. After analyzing the data, they obtain a p-value and compare it with that prespecified threshold. They then describe whether the result is statistically significant and interpret that outcome with confidence intervals, effect size, sample size, and replication evidence. This workflow separates the statistical decision from broader scientific judgment.
In psychology, a threshold-based label is one part of evaluating evidence, not a final statement about a research claim. Investigators should examine the associated effect size and confidence intervals, consider the study’s sample size, and look for replication evidence. This broader context helps determine whether an apparently unusual result is robust and scientifically meaningful rather than overstating a single finding.