The Chi-Square test is a statistical test used to compare observed data with expected data. It is commonly used to determine if there is a significant difference between the two sets of data.

For example, let’s say a researcher wants to test if there is a significant difference in the number of red and blue jellybeans in a jar. The researcher counts the number of red and blue jellybeans and compares it to the expected number of red and blue jellybeans based on the total number in the jar. If the observed and expected numbers are significantly different, the researcher can conclude that there is a difference in the number of red and blue jellybeans in the jar.

Another example is a market researcher who wants to test if there is a significant difference in the percentage of men and women who prefer a certain brand of shampoo. The researcher conducts a survey and asks participants if they prefer the brand of shampoo. The results are then compared to the expected percentage of men and women who would prefer the brand based on the total population. If the observed and expected percentages are significantly different, the researcher can conclude that there is a difference in the preference for the brand of shampoo between men and women.

In both examples, the Chi-Square test is used to determine if there is a significant difference between the observed and expected data. The test calculates the difference between the two sets of data and determines if it is large enough to be considered significant. If the difference is significant, the researcher can conclude that there is a difference in the observed and expected data. If the difference is not significant, the researcher can conclude that there is no difference between the observed and expected data.

Overall, the Chi-Square test is a useful tool for comparing observed and expected data and determining if there is a significant difference between the two. It is commonly used in a variety of research areas, including psychology, sociology, and biology.