Test of goodness of fit

the most common goodness of fit tests:--

Broadly, the goodness of fit test categorization can be done based on the distribution of the predictand variable of the dataset.

1. The chi-square
2. Kolmogorov-Smirnov
3. Anderson-Darling

The Chi-Square Goodness of Fit Test

Chi-square goodness of fit test is conducted when the predictand variable in the dataset is categorical. It is applied to determine whether sample data are consistent with a hypothesized distribution.

Chi-Square test can be applied when the distribution has the following characteristics

• The sampling method is random.
• Predictand variables are categorical.
• The expected value of the number of sample observations at each level of the variable is at least 5. It requires a sufficient sample size for the chi-square approximation to be valid.

Merits of the Chi-square Test

• A distribution-free test. It can be used in any type of population distribution.
• It is widely applicable not only in social sciences but in business research as well.
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