# Confidence Intervals for Population Parameters

In the previous chapters we considered how it is possible to estimate the parameters of the population and check the quality of the data of the estimates. But those estimates were **point**: we simply determined the possible value of the parameter based on the data we have. But there is another approach: we can construct a certain interval that, with some probability, covers the real value of the desired parameter. This interval is called the **confidence interval**.
Let's look at the definition:

The principle of constructing confidence intervals is somewhat **similar to the principle of constructing point estimates**. We also use a certain function with our samples as arguments for this function. That we use the distribution law of this function and build an interval. But a rigorous mathematical explanation of this process can be quite complicated, so we will not stop on it in more detail.

NoteIt's worth noting that there's another type of interval estimation for population parameters called the

credible interval, which is constructed using the Bayesian theorem. These intervals have different interpretations:

- The confidence interval is essentially an interval with
random endpointsthat, with a certain probability,covers the true constant valueof the parameter;- In contrast, the credible interval is a
constant intervalwhere therandom value of the desired parameter fallswith a certain probability.

## Confidence interval for Gaussian distribution expectation parameter

Let's look at how to build a confidence interval for **Gaussian distribution expectation parameter**. We will consider 2 different situations:

In the image above, we provided a confidence interval for Gaussian expectation if we know variance. We use the PPF of Gaussian distribution and sample to build this interval.

Then we provided a confidence interval for Gaussian expectation if we don't know the variance and used adjusted sample variance instead of known variance for estimation. We use the PPF of Student distribution with an `n-1`

degree of freedom to build this interval.

## Confidence interval with Python

Let's now look at how to build a confidence interval for the mean value of Gaussian samples in Python. We will use different confidence levels and compare intervals built due to corresponding confidence levels.

We see that the higher the confidence level, the wider the interval we get. This is quite logical, since **the wider the interval**, the **higher the probability** that this interval covers the real value of the mean.

Everything was clear?

Course Content

Probability Theory Mastering

## Probability Theory Mastering

1. Additional Statements From The Probability Theory

3. Estimation of Population Parameters

4. Testing of Statistical Hypotheses

# Confidence Intervals for Population Parameters

In the previous chapters we considered how it is possible to estimate the parameters of the population and check the quality of the data of the estimates. But those estimates were **point**: we simply determined the possible value of the parameter based on the data we have. But there is another approach: we can construct a certain interval that, with some probability, covers the real value of the desired parameter. This interval is called the **confidence interval**.
Let's look at the definition:

The principle of constructing confidence intervals is somewhat **similar to the principle of constructing point estimates**. We also use a certain function with our samples as arguments for this function. That we use the distribution law of this function and build an interval. But a rigorous mathematical explanation of this process can be quite complicated, so we will not stop on it in more detail.

NoteIt's worth noting that there's another type of interval estimation for population parameters called the

credible interval, which is constructed using the Bayesian theorem. These intervals have different interpretations:

- The confidence interval is essentially an interval with
random endpointsthat, with a certain probability,covers the true constant valueof the parameter;- In contrast, the credible interval is a
constant intervalwhere therandom value of the desired parameter fallswith a certain probability.

## Confidence interval for Gaussian distribution expectation parameter

Let's look at how to build a confidence interval for **Gaussian distribution expectation parameter**. We will consider 2 different situations:

In the image above, we provided a confidence interval for Gaussian expectation if we know variance. We use the PPF of Gaussian distribution and sample to build this interval.

Then we provided a confidence interval for Gaussian expectation if we don't know the variance and used adjusted sample variance instead of known variance for estimation. We use the PPF of Student distribution with an `n-1`

degree of freedom to build this interval.

## Confidence interval with Python

Let's now look at how to build a confidence interval for the mean value of Gaussian samples in Python. We will use different confidence levels and compare intervals built due to corresponding confidence levels.

We see that the higher the confidence level, the wider the interval we get. This is quite logical, since **the wider the interval**, the **higher the probability** that this interval covers the real value of the mean.

Everything was clear?