Cross Tabulation

Cross tabulations, commonly known as cross tabs, involve the analysis of categorical variables. Categorical variables represent distinct categories or groups, such as gender, age groups, product types, or geographic regions.
The primary output of a cross-tabulation is a table that displays the frequencies, counts, averages or other measures of observations that fall into the intersections of the categories of the variables being analyzed. The table is organized in rows and columns, with each cell representing the count or frequency of observations for a specific combination of categories.

Cross tabs are a powerful technique used to analyze the differences between groups. They comprehensively organize and interpret data, offering valuable insights into patterns, trends, and associations.

Significance tests play a crucial role in cross-tabulations by helping to determine whether the differences between variables are statistically significant or if they could have occurred by chance.

Testing the significance of a sample proportion is used to answer whether the breakdown column sample proportion is significantly greater or smaller than the total population proportion (one-tail test).

Colouring the cells or values of groups significantly higher or lower than the total values makes it easier to distinguish them visually.
Our significance tests use Significance Level 0.05 (5%) as the standard, the most commonly used level in statistical testing. The significance test level refers to the predetermined thresholds used to determine whether the statistical test results are considered statistically significant. A significance level of 0.05 indicates a 5% chance (1 in 20) of rejecting the null hypothesis when it is true (Type I error).
Other common Significance Levels are 0.01 (1%) (lower probability of making Type I errors) and 0.1 (10%) (lower probability of making Type I errors).