A children's book author tested whether cover design affects sales by releasing two versions with different cover types. Using the Chi-square test for independence, he collected data showing 320 books with low-cost covers sold versus 350 with high-cost covers, while 180 low-cost covers didn't sell compared to 150 high-cost covers.

Python analysis revealed a chi-square statistic of 4.07 with a p-value of 0.043, falling below the 0.05 significance threshold. This suggests the cover type and sales outcomes are statistically associated rather than independent. The test works by comparing observed sales data against expected counts calculated under the assumption that cover design has no effect on purchasing decisions.

Expected counts are derived from overall sales probabilities: 67% of all books sold, meaning 335 of each cover type should sell if cover design doesn't matter. The Chi-square statistic measures how much observed counts deviate from these expected values, with squaring differences preventing positive and negative deviations from canceling each other out. This statistical approach helps determine whether observed differences reflect genuine patterns or random variation.