Global Market Research

Chi-square

Back to Glossary

Chi-square

Otherwise referred to as the X2 test, the Chi-Square test is a statistical hypothesis test for the measurement and definition of sample distribution. This form of test is commonly used to determine the difference in results of various category components and whether or not they are statistically significant in their frequency.
 
This test is particularly useful when trying to establish why the number of results for a particular category is higher than expected, and whether the difference between the determined and the observed frequencies is due to factors such as sample size, or whether it is a real notable difference.  

The unique feature of the Chi-square test is that it deals with the difference in categorical variables, as opposed to numerical variables, for instance a question that can be answered with a category includes things like gender and region, rather than numbers such as: age, height or weight.  The test then compares the counts of these responses between at least two independent groups in order to provide a distribution statistic.

This test can often be used to compare the deviation of results observed within the quantitative research of a particular sample in comparison to the standard deviation witnessed within the population, allowing users to determine whether an association may exist between two particular categories.

An example of how this test could be utilised when looking at market research data could include a healthcare authority for instance, using the test to try and establish whether or not people in the North West have a higher frequency of developing a particular heart condition, in comparison to the rest of the UK. Another possible use may be for a telecoms company trying to determine if there is a significant variation in the preference for a particular brand of phone in women compared to men. Therefore the Chi-square test really allows you to determine the true significance in your research results, helping you to accurately interpret the research and identify the key stories within your study.

Support Us..

We hope that you have found this article useful. This section is freely available for all to use. Please help support it by liking us or following us on our social media platforms:

Share this article..

For updated Chi-square information please follow us on @djsresearch.

© DJS Research 2017