Differences Analysis: Types and Uses

The comparison of differences among groups or categories is one of the most foundational reasons why marketing research is conducted. The analysis of differences can help you or your organization segment more efficiently, understand the different types of marketing content to create, predict the kinds of behaviors your audience will engage in, and so much more. Comparing means is a commonly used tool in research, and the different types of means comparisons have an immense number of applications in marketing. Analyzing those differences to ensure they are applicable will take a different form depending on the type of comparison you are making. Let’s take a look at three common types of statistical analyses used for this purpose.

Independent Samples t-Test

This type of statistical analysis is used in comparing the means from two separate sub-groups within the testing sample. So, for example, if you wanted to compare how many dollars in merchandise your female customers purchase versus your male customers, you would utilize this method. It would allow you to determine whether any difference in the mean dollar amount spent is statistically significant enough to represent a big enough variation that you can then act upon.

Paired t-Test

This second type of analysis is for comparing the same group across two different dimensions, states, or points in time. One example of this would be in comparing the average level of one single group’s feelings toward a product before and after showing them an advertisement or marketing campaign. This would allow you to determine if the before and after difference is significant enough to mean that your ad or campaign is effective at raising your audience’s positive feelings toward your product.


This analysis type is for when you have more than 2 means to compare. This could be an extension of either of the two other types of comparisons listed above, but with 3 or more means to compare.

These tests are important to use when making comparisons across means because simply putting one number next to another will not tell you whether the difference between them is meaningful enough to justify you acting upon it. And as we know, in marketing, we often have to justify the investment into research by demonstrating its value.

Next steps: If you would like to see more on statistical tests, check out this article: Statistical Tests — When to use Which?

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