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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.

Diese Übung ist Teil des Kurses

Customer Analytics and A/B Testing in Python

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Anleitung zur Übung

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.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# 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")
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