Suppose we’re running an A/B Test to find out if a new website layout drives more subscriptions than the current one. If the new layout is only a tiny percent better, would we really care?

In order to detect precise differences, we need a very large sample size. In order to choose a sample size, we need to know the smallest difference that we actually care to measure. This “smallest difference” is our desired *minimum detectable effect*. This is also sometimes referred to as *desired lift*.

Minimum detectable effect or lift is generally expressed as a percent of the baseline conversion rate. Suppose that 6% of customers currently subscribe to our website (that’s our baseline conversion rate). Changing a website layout is hard, so we only think that it’s worth doing if at least 8% of our customers would subscribe with the new layout. To calculate this as a percentage of our baseline:

baseline = 6 new = 8 min_detectable_effect = (new - baseline) / baseline * 100 print(min_detectable_effect) #output: 33.0

Our minimum detectable effect/desired lift is 33%.

### Instructions

**1.**

Suppose you have a baseline conversion rate of `8%`

and want to implement a new website design if it will increase the conversion rate to `12%`

. What is your minimum detectable effect? Save that number into a variable called `mde`

and print it out.