Chi Square Test
- Compares observed counts or proportions against expected counts or proportions.
- Determines whether the discrepancy between observed and expected data is large enough to be considered significant.
- Applied to practical problems such as counting categories (e.g., colors, preferences) and comparing observed proportions to expected proportions.
Definition
Section titled “Definition”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.
Explanation
Section titled “Explanation”The test evaluates the differences between observed and expected values and determines whether those differences are large enough to be considered significant. When the calculated difference meets the criterion for significance, a researcher concludes there is a difference between observed and expected data. If the difference is not significant, the researcher concludes there is no difference.
Examples
Section titled “Examples”Jellybean color counts
Section titled “Jellybean color counts”A researcher counts the number of red and blue jellybeans in a jar and compares those observed counts to the expected numbers based on the total in the jar. If the observed and expected numbers are significantly different, the researcher concludes there is a difference in the number of red and blue jellybeans.
Shampoo preference by gender
Section titled “Shampoo preference by gender”A market researcher surveys participants about preference for a certain shampoo brand and compares the observed percentages of men and women who prefer the brand to the expected percentages based on the total population. If the observed and expected percentages are significantly different, the researcher concludes there is a difference in preference between men and women.
Use cases
Section titled “Use cases”- Psychology
- Sociology
- Biology
Notes or pitfalls
Section titled “Notes or pitfalls”- The test calculates the difference between observed and expected data and assesses whether that difference is significant.
- If the difference is not significant, the conclusion is that there is no difference between the observed and expected data.
Related terms
Section titled “Related terms”- Observed data
- Expected data