Checking for statistical significance
Now that you have an intuitive understanding of statistical significance and p-values, you will apply it to your test result data.
The four parameters needed for the p-value function are the two conversion rates - cont_conv and test_conv and the two group sizes - cont_size and test_size. These are available in your workspace, so you have everything you need to check for statistical significance in our experiment results.
This exercise is part of the course
Customer Analytics and A/B Testing in Python
Exercise instructions
Find the p-value of our experiment using the loaded variables cont_conv, test_conv, cont_size, test_size calculated from our data. Then determine if our result is statistically significant by running the second section of code.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute the p-value
p_value = get_pvalue(con_conv=____, test_conv=____, con_size=____, test_size=____)
print(p_value)
# Check for statistical significance
if p_value >= 0.05:
print("Not Significant")
else:
print("Significant Result")