In what situation would you use inferential statistics?

Using inferential statistics is particularly important when you want to analyze sample data and make inferences about a larger population. This approach allows statisticians and researchers to draw conclusions and make predictions about a whole population based on a representative subset of it, rather than needing to collect data from every single member of that population.

For example, if a researcher wants to understand the voting preferences of an entire country, conducting a survey of every citizen would be impractical and costly. Instead, by studying a random sample of voters, the researcher can apply inferential statistical techniques to estimate the preferences of the broader population, accounting for sampling errors and making broader generalizations. This is a crucial step since it allows for understanding trends and making decisions without the limitations of total population data collection.

In contrast, presenting numerical information about a sample, visualizing data distributions, or measuring central tendency primarily focus on descriptive statistics, which summarize and describe data without making predictions or inferences beyond the data at hand. These approaches provide valuable insights but do not extend conclusions to larger populations in the way inferential statistics does.