The goodness of fit calculator helps you determine how well your observed data match the expected distribution. This tool provides calculations for both the Chi-Square Test and the Kolmogorov-Smirnov Test.

## Chi-Square Test for Goodness of Fit

The Chi-Square Test is a statistical method to determine if there is a significant difference between the expected and observed frequencies in categorical data. The test compares the observed data with data that we would expect to obtain according to a specific hypothesis.

To calculate the Chi-Square value, use the following formula:

χ² = Σ((O - E)² / E)

Where:

**O**is the observed frequency**E**is the expected frequency**Σ**indicates the sum over all categories

## Kolmogorov-Smirnov Test

The Kolmogorov-Smirnov Test is used to determine the goodness of fit for continuous distributions. It compares the cumulative distribution function of the sample data with the expected distribution. This test is sensitive to both the location and shape of the empirical cumulative distribution function.

## Understanding Goodness of Fit

Goodness of fit is a measure of how well a statistical model fits a set of observations. It summarizes the discrepancy between observed values and the values expected under the model in question. In the context of statistical analysis, goodness of fit tests help in validating the model used to describe the data distribution. Both the Chi-Square and Kolmogorov-Smirnov tests are widely used for this purpose.

## Steps to Calculate Goodness of Fit

Follow these steps to calculate the goodness of fit:

**Collect Data:**Gather observed data and expected frequencies (or distribution).**Select Test:**Choose between the Chi-Square