The significance level determines which column of a chi-square table you consult, so the same test statistic can lead to different decisions under different chosen thresholds. Researchers should select the significance level before interpreting the result, then compare the calculated statistic with the critical value associated with that level and the correct degrees of freedom.
Degrees of freedom determine the specific table entry used for comparison. They must be identified from the design of the chi-square analysis before the critical value is read; using an incorrect value can pair the test statistic with the wrong cutoff. This is why calculating the statistic alone is insufficient for interpreting a chi-square result.
Interpretation depends on the relationship between the calculated statistic and its table value, not on the statistic in isolation. When the statistic exceeds the relevant critical value, the result supports rejecting the null hypothesis under the selected significance level; otherwise, the comparison does not provide that basis for rejection. The conclusion combines all three factors.
Although the same reference framework appears across chi-square analyses, the underlying question differs. A test of independence examines whether categorical variables are associated, goodness-of-fit evaluates how observed results align with an expected pattern, and a test of variance addresses variability. The degrees of freedom and calculated statistic must match the specific analysis before table lookup.
Begin by calculating the chi-square test statistic, then determine the degrees of freedom and select the significance level required for the analysis. Locate the intersection of that level and degrees of freedom in the table, and compare the statistic with the listed critical value. This sequence keeps the numerical result connected to the correct decision criterion.
For categorical-data studies, the table helps evaluate whether an observed association between variables is unlikely to have occurred by chance. The researcher uses the statistic produced by the independence analysis, matches its degrees of freedom and significance level to the table, and bases the conclusion on the comparison. Thus, the table supports interpretation rather than replacing the analysis.
In a goodness-of-fit analysis, the reference value helps place the difference between observed results and an expected pattern into a significance framework. After calculating the chi-square statistic, the researcher identifies the relevant degrees of freedom and significance level before consulting the table. The comparison indicates whether the observed departure is sufficient to reject the null hypothesis.
For a test of variance, the table provides the critical benchmark used to interpret the calculated chi-square statistic. Its role is to connect the observed variability with a null-hypothesis decision at a selected significance level and degrees of freedom. This application shows that chi-square tables extend beyond categorical association and goodness-of-fit questions within statistics.