A/B testing (sometimes called split testing) is comparing two versions of a web

Q: A/B testing (sometimes called split testing) is comparing two versions of a web

a) p - value is less than 0.01 (Significance level) so we reject the null hypothesis. Hence, There is enough evidence to conclude that the two proportions are not equal. b) 99% confidence interval: (0.07514, 0.08911) Interpretation: We are 99% confident that the difference in the proportions lie betweeen 0.07514 and 0.08911.

ID: 3364352 • Letter: A

Question

A/B testing (sometimes called split testing) is comparing two versions of a web page to see which one performs better. To increase Barack Obama’s visibility and to raise money for the campaign leading up to the 2008 presidential election, Obama’s analytics team conducted an A/B test with his website. In the original version, the button to join the campaign read “Sign up”. In an alternative version, it read “earn More”. Each visitor is randomly sent to one of the two versions of the website. Of 77,858 visitors to the original version, 5851 clicked the button. Of 77,729 visitors to the alternative version, 6927 clicked the button. Let p1 represent the population proportion of those who join the campaign in version A website (“Sign up”) and p2 represent that in version B website (“Learn More”). Use the R output below to answer the following questions.

> prop.test(x=c(5851, 6927), n=c(77858, 77729), conf.level=0.99, alternative="two.sided")

2-sample test for equality of proportions

data: c(5851, 6927) out of c(77858, 77729)

X-squared = 100.67, df = 1, p-value < 2.2e-16

alternative hypothesis: two.sided

99 percent confidence interval: -0.01755 -0.01038

sample estimates: prop 1 prop 2

0.07514 0.08911

a State the null and alternative hypotheses to test whether there is a significant difference in two participation rates. Use the p-value shown above to draw conclusion. (NO need to calculate test statistics or p-value)

b Interpret the 99% confidence interval shown above

Explanation / Answer

a) p - value is less than 0.01 (Significance level) so we reject the null hypothesis. Hence,

There is enough evidence to conclude that the two proportions are not equal.

b) 99% confidence interval: (0.07514, 0.08911)

Interpretation:

We are 99% confident that the difference in the proportions lie betweeen 0.07514 and 0.08911.

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A/B testing (sometimes called split testing) is comparing two versions of a web

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