Descriptive vs Inferential Statistics: What’s the Difference?

Descriptive vs Inferential Statistics: What’s the Difference?

Statistics are used to help people gain insights and make decisions from data. There are two main branches of statistics, descriptive and inferential statistics. It's important to understand the differences between these two types of statistics so you can choose the best type for your analysis.

Descriptive Statistics

Descriptive statistics is used to summarize a set of data. It describes the data by looking at measures of central tendency like mean, median, and mode. It also looks at measures of variation, such as standard deviation, range, and percentiles. These measures help to provide an overall picture of the data, allowing us to quickly communicate the most important information. It can also help identify patterns and trends in the data.

Inferential Statistics

Inferential statistics allow us to draw conclusions from a sample of data. This means that we can make predictions or judgments about a larger population using the limited data that we have collected. The goal of inferential statistics is to use a subset of data to infer something about the whole. It uses methods such as hypothesis testing, correlation, and regression to make conclusions.

Conclusion

It's important to understand the differences between descriptive and inferential statistics when choosing the right type for your data. Descriptive statistics is used to summarize and describe a set of data, while inferential statistics allows us to make predictions about a larger population from a sample of data. Knowing when and how to use each type of statistic will help you get the most out of your data.