Calculate the **confidence interval** of the proportion sample using the **Normal distribution approximation** for the binomial distribution and a better method, the **Wilson score interval**.

example digits=2: (0.001234 => 0.0012).

The probability that the **true value** of the estimated parameter will be in the confidence interval.

Sample size.

Enter the sample proportion p̂ or the total number of succcesses.

**Proportion Confidence interval**

When using the sample data, we know the proportion sample statistic but we don't know the true value of the population's proportion. Instead, we may treat the population's measures as random variables and calculate the confidence interval.

First, we need to define the **confidence level** which is the required certainty level that the true value will be in the **confidence interval** Researchers commonly use a confidence level of **0.95**.

The Wilson score interval supports a better result than the normal approximation interval, especially for small samples and for edge proportions near 0 or 1.