Confidence Interval (CI) is essential in statistics and very important for data scientists. In this article, I will explain it thoroughly with necessary formulas and also demonstrate how to calculate it using python.

Confidence Interval

As it sounds, the confidence interval is a range of values. In the ideal condition, it should contain the best estimate of a statistical parameter. It is expressed as a percentage. 95% confidence interval is the most common. You can use other values like 97%, 90%, 75%, or even 99% confidence interval if your research demands. Let’s understand it by an example:

Here is a statement:

“In a sample of 659 parents with toddlers, about 85%, stated they use a car seat for all travel with their toddler. From these results, a 95% confidence interval was provided, going from about 82.3% up to 87.7%.”

This statement means, we are 95% certain that the population proportion who use a car seat for all travel with their toddler will fall between 82.3% and 87.7%. If we take a different sample or a subsample of these 659 people, 95% of the time, the percentage of the population who use a car seat in all travel with their toddlers will be in between 82.3% and 87.7%.

Remember, 95% confidence interval does not mean 95% probability

The reason confidence interval is so popular and useful is, we cannot take data from all populations. Like the example above, we could not get the information from all the parents with toddlers. We had to calculate the result from 659 parents. From that result, we tried to get an estimate of the overall population. So, it is reasonable to consider a margin of error and take a range. That’s why we take a confidence interval which is a range.

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