Exercise

# Power Analysis - Part II

Previously, we simulated one instance of the experiment & generated a p-value. We will now use this framework to calculate statistical power. Power of an experiment is the experiment's ability to detect a difference between treatment & control if the difference really exists. It's good statistical hygiene to strive for 80% power.

For our website, suppose we want to know how many people need to visit each variant, such that we can detect a 10% increase in time spent with 80% power. For this, we start with a small sample (50), simulate multiple instances of this experiment & check power. If 80% power is reached, we stop. If not, we increase the sample size & try again.

Instructions

**100 XP**

- For the
`time_spent`

random variables, set`size`

as tuples such that shape is`sample_size`

\(\times\)`sims`

. - Calculate
`power`

as a fraction of p-values less than 0.05 (statistically significant). - If
`power`

is**greater than or equal to**80%,`break`

out of the while loop. Else, keep incrementing`sample_size`

by 10.