Chi Square Goodness of Fit Test
Chi Square Goodness of Fit Test |
Description | Chi Square Goodness of Fit Test determines whether a categorical variable is likely to be derived from a specified distribution. This test is the same as Pearson’s Chi Square test. |
Why to use | To check whether a sample data derived from a population is a representative of the population. |
When to use | For categorical variables | When not to use | For continuous variables |
Prerequisites | - The data should be categorical.
- There should be at least five values in each of the observed data categories.
- The data should be a random sample of the population.
- There should be a hypothesis describing how the variable is distributed.
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Input | One categorical variable | Output | - Chart of Contribution to Chi Square value by category
- Charts of Observed and Expected values
- Null and Alternative Hypothesis
- Computation and Result tables for Chi square
- Interpretation of the result
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Statistical Methods used | - Frequency
- p-value
- alpha
- Chi Square
| Limitations | It can be used only on categorical data.
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The Chi Square Goodness of Fit Test is a hypothesis test. It tests whether the selected categorical variable is likely to be derived from the specified distribution. A dataset consists of data points. You also have a hypothesis or an idea to imagine how these data points are distributed in the dataset. The Chi Square Goodness of Fit Test gives you a way to check whether the data points actually fit our idea or hypothesis. That is, the test checks whether the data points are really distributed the way you have imagined them to be.
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