Calculating a confidence interval
If you give a single estimate of a sample statistic, you are bound to be wrong by some amount. For example, the hypothesized proportion of late shipments was 6%. Even if evidence suggests the null hypothesis that the proportion of late shipments is equal to this, for any new sample of shipments, the proportion is likely to be a little different due to sampling variability. Consequently, it's a good idea to state a confidence interval. That is, you say, "we are 95% 'confident' that the proportion of late shipments is between A and B" (for some value of A and B).
Sampling in Python demonstrated two methods for calculating confidence intervals. Here, you'll use quantiles of the bootstrap distribution to calculate the confidence interval.
late_prop_samp and late_shipments_boot_distn are available; pandas and numpy are loaded with their usual aliases.
This exercise is part of the course
Hypothesis Testing in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate 95% confidence interval using quantile method
lower = ____
upper = ____
# Print the confidence interval
print((lower, upper))