A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.

This tutorial explains how to plot a confidence interval for a dataset in Python using the seaborn visualization library.

The notched boxplot allows you to evaluate confidence intervals (by default 95% confidence interval) for the medians of each boxplot. To create the notch, set notch=True in the plt.boxplot function. # Notched box plot plt.boxplot(df'A',notch= True); Plotting boxplot using seaborn. Lets look into an existing dataset – Titanic Dataset. The output is then visualized by plotting the confidence interval and the corresponding hypothetical norm value Stack Exchange Network Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. I think you are confusing several things here. The 95% confidence interval for any value of a normal distribution is just that, the interval in which 95% of the values land. What you seem to be conflating with is the 95% confidence interval for a mean calculated from a sample. – MaxNoe Sep 4 '20 at 12:48.

Plotting Confidence Intervals Using lineplot()

The first way to plot a confidence interval is by using the lineplot() function, which connects all of the data points in a dataset with a line and displays a confidence band around each point:

By default, the lineplot() function uses a 95% confidence interval but can specify the confidence level to use with the ci command.

The smaller the confidence level, the more narrow the confidence interval will be around the line. For example, here’s what an 80% confidence interval looks like for the exact same dataset:

Plotting Confidence Intervals Using regplot()

You can also plot confidence intervals by using the regplot() function, which displays a scatterplot of a dataset with confidence bands around the estimated regression line:

Similar to lineplot(), the regplot() function uses a 95% confidence interval by default but can specify the confidence level to use with the ci command.

Pandas Confidence Interval

Again, the smaller the confidence level the more narrow the confidence interval will be around the regression line. For example, here’s what an 80% confidence interval looks like for the exact same dataset:

Additional Resources

Plot 95 Confidence Interval Python Example

What are Confidence Intervals?
How to Calculate Confidence Intervals in Python