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4. Significance testing

We can help advise on the appropriate significance testing for a data set.

Very commonly it might be to test for significant differences between percentages, or means scores, for two independent samples using a t-test. Relevant to the small universe sizes we often work with in pharmaceutical research, we can advise on whether the finite population correction is likely to make a noticeable impact.

Sometimes other tests are more appropriate, and we can advise on this – such as with the use of chi-square, paired-samples t-tests, one-way ANOVA and when to use nonparametric tests.

Overall, we recognise that significance testing is a technical concept not always clearly understood by market research users. Our edge is our experience of explaining the relevance of significance testing in layman’s terms!