Usually, the decisions we make from our dataset are influenced by the output of statistical measures we apply to them. Those outputs tell us about the type of correlation and the basic visualizations of the dataset. However, sometimes, the decisions differ when we segregate the data into groups and apply statistical measures, or when we aggregate it together and then apply statistical measures. This kind of anomalous behavior in the results of the same dataset is generally called Simpson's paradox. Put simply, Simpson's paradox is the difference that appears in a trend of analysis when a dataset is analyzed in two different situations: first, when data is separated into groups and, second, when data is aggregated.
Here's a table that represents the recommendation rate for two different game consoles by males and females individually...
