Up to now in our study of the basics of Statistics we’ve covered descriptive statistics. Click on the previous link in the previous sentence for the series on introduction to statistics and descriptive statistics. Now we’ll move into inferential statistics. The next series of posts is on confidence intervals. After the confidence intervals series we have a series on hypothesis testing.

Inferential Statistics

Inferential statistics refers to methods that rely on probability theory and distributions in particular to predict population values based on sample data. This topic involves point estimates and confidence intervals. Inferential statistics allow data professionals to make inferences about a dataset based on a sample of the data. For example, you could use inferential statistics to estimate the average weight of all college students in the United States based on a sample of 1,000 students. Inferential statistics includes hypothesis testing. It also includes simple linear regression.

Review of Descriptive Statistics

Descriptive statistics are used to summarize and describe essential features of a dataset. This was discussed in the previous series called Statistics. What are some of the topics of descriptive statistics?

• Measures of central tendency (mean, median, mode)
• Asymmetry (skewness)
• Measures of variability (variance, standard deviation, coefficient of variation)
• Graphical tools (charts, histograms)

If you are working with a dataset in Python, you will typically always want to run the describe() function.