Chi-Square Statistic
Definition
The chi-square statistic is a measure used in hypothesis testing to determine if there is a significant difference between observed and expected frequencies in categorical data. It quantifies how much the observed data deviates from what would be expected under the null hypothesis.
Analogy
Imagine you are comparing the number of people who prefer different genres of music in two cities. The chi-square statistic would tell you if there is a significant difference between the observed distribution of preferences and what you would expect based on chance alone.
Related terms
Null Hypothesis: A statement that assumes no relationship or difference between variables.
P-value: The probability of obtaining results as extreme as, or more extreme than, the observed data assuming the null hypothesis is true.
Significance Level: The threshold used to determine whether to reject or fail to reject the null hypothesis.
"Chi-Square Statistic" appears in:
Study guides (2)
Additional resources (1)
Practice Questions (3)
- The chi-square statistic in a chi-square goodness of fit test is calculated using which formula?
- If the chi-square statistic is greater than the critical value in a chi-square goodness of fit test, what can be concluded about the null hypothesis?
- Which formula is used to calculate the chi-square statistic in a chi-square test?
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