Goodness-of-Fit Tests are statistical methods used to determine how well a set of observed data matches a specific theoretical distribution. These tests help assess whether the differences between observed and expected frequencies are due to random chance or indicate a significant deviation from the expected model. Common examples include the Chi-Square Test and the Kolmogorov-Smirnov Test. These tests are essential in various fields, including social sciences, biology, and finance, as they provide insights into the validity of models and assumptions. By evaluating the fit of data, researchers can make informed decisions and improve their analyses.