The A/B testing is commonly used nowadays for advertisement. Two customers can visit a retail webpage, and each sees different content.
The goal here is to select to measure which advertisement is effective.
If we have picked 1000 visitors randomly, 900 clicked on advertisement A, and 100 clicked on advertisement B, we can conclude that advertisement A is better.
What if the proportions are different? What if advertisement A got 400 clicks and advertisement B got 360 clicks. Which one do you choose, and how confident are you?
The question becomes:
Is there a significant difference between the population proportions of the population a and population b?
Null hypothesis (H0): pa — pb = 0
Alternative hypothesis (Ha): pa — pb ≠ 0
Alpha*: 0.05* The threshold we use to decide if the p-value is sufficiently low to reject the null hypothesis.
Since the views are independent, and we can view each as a Bernoulli trial, we can generate that data as follows:
n = 1000
pa = 400 / n
pb = 360 / npopulation1 = binom.rvs(1, pa, size=n)
population2 = binom.rvs(1, pb, size=n)ttest_ind(population1, population2)#output: (statistic=0.09168604722034365, pvalue=0.9269566745658202)
The t-test score is 0.092, and the p-value is 0.92.
Since 0.92 > 0.05, we can’t reject the null hypothesis, which means there is no statistical significance to favor one advertisement over the other.
Now, what if advertisement b got 340 clicks instead of 360:
population2 = binom.rvs(1, pb, size=n)ttest_ind(population1, population2)#output: (statistic=2.135431894379663, pvalue=0.03284703858914734)
The t-test score is 2.135, and the p-value is 0.033.
Since 0.033 < 0.05, we can reject the null hypothesis, which means there is a statistical significance to favor advertisement a.
As you can see, statistics has powerful tools to make better decisions with confidence. Thus when in doubt, t-test it.
Link to GitHub: