**What is it?**

A confidence interval is a range of estimated values for an unknown parameter. To calculate it, we have to define the confidence level: the 95% confidence level is the most common. Also, 90% and 99% confidence levels are used too.

**Confidence of 95%?**

The 95% confidence level means that in 95% of cases, the estimate will fall between the lower and upper bound of the interval.

The most common value for the confidence interval is **95%**, but **99%** and **90%** are used too.


**Alpha value**

The confidence interval is usually equal to 1-a. Where alpha is the probability of a type I error. Type I errors describe cases when we reject a null hypothesis that is true.

**Null and alternative hypotheses**

The alternative and null hypotheses are assumptions used in statistical tests to make judgments based on data. In statistical hypothesis testing, the null and alternative hypotheses are mutually exclusive statements. Usually, in hypothesis testing, we try to reject or not the null hypothesis.

In this section, we will deal with the data about penguins. At the end of the section, we will say that with *95% confidence, the mean penguin's mass is...*

This section will help us deal with the first real statistical case: finding confidence intervals. It requires knowledge of NumPy, pandas, Matplotlib, and Seaborn library to calculate math formulas and build visualization! To encourage you to pass this section, I want to point out that you will run across a small amount of theory but a significant amount of practice!

An inseparable part of a data analyst's life is conducting hypothesis testing. After completing this section, you will understand the idea behind testing in statistics and will be able to conduct a t-test using Python.