# How to compute a confidence interval for the population proportion (in Python, using SciPy)

## Task

If we have a sample of qualitative data from a normally distributed population, then how do we compute a confidence interval for a population proportion?

Related tasks:

- How to compute a confidence interval for a mean difference (matched pairs)
- How to compute a confidence interval for a regression coefficient
- How to compute a confidence interval for a population mean
- How to compute a confidence interval for a single population variance
- How to compute a confidence interval for the difference between two means when both population variances are known
- How to compute a confidence interval for the difference between two means when population variances are unknown
- How to compute a confidence interval for the difference between two proportions
- How to compute a confidence interval for the expected value of a response variable
- How to compute a confidence interval for the ratio of two population variances

## Solution

We’re going to use some fake data here for illustrative purposes, but you can replace our fake data with your real data in the code below.

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# Replace the next two lines of code with your real data
sample_size = 30
sample_proportion = .39
# Find the margin of error
from scipy import stats
import numpy as np
alpha = 0.05 # replace with your chosen alpha (here, a 95% confidence level)
moe = stats.norm.ppf(1-alpha/2) * np.sqrt(sample_proportion*(1-sample_proportion)/sample_size)
# Find the confidence interval
lower_bound = sample_proportion - moe
upper_bound = sample_proportion + moe
lower_bound, upper_bound

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(0.21546413420702207, 0.564535865792978)

Our 95% confidence interval is $[0.2155, 0.5645]$.

Content last modified on 24 July 2023.

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Contributed by Elizabeth Czarniak (CZARNIA_ELIZ@bentley.edu)