The chi-square test is a non-parametric statistical tool widely used in business research for testing relationships between categorical variables and for assessing the goodness-of-fit between observed and expected frequencies. To apply the chi-square test accurately, certain conditions must be met to ensure the validity and reliability of the results. One primary condition is that the data must be in the form of frequencies or counts rather than percentages, ratios, or continuous data. The chi-square test is designed specifically for categorical data, such as gender, occupation, product preference, or market segment, where the variables are nominal or ordinal in nature. Another crucial condition is the independence of observations. Each observation in the dataset should be independent of the others, meaning that the occurrence of one observation should not influence or depend on another. This assumption ensures that the frequencies in each category are mutually exclusive and do not overlap, which is essential for accurate calculation of the chi-square statistic. For example, in a survey on customer