Use minimum detectable effect (MDE) when designing a test
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 Estimate how long a test will take
 Decide how "sensitive" an experiment should be
 Decide how many variations to run
Once you decide on a hypothesis, you’ll design an experiment. How many variations should you create? What kind of test should you run: A/B, multivariate, or multipage?
Experiment design is important, because it's a key part of the cost calculation of experimentation. The design and scope of your experiment determine how long it will take to reach statistical significance.
Use this information to consider:

Are the results of this experiment likely valuable enough to justify the amount of traffic or time? Are other, potentially more impactful ideas that you could be testing?

Should you reduce the number of variations to speed up my test? If so, how would you redesign this experiment?

Should you increase the drama  or degree of difference  between the variation and the original to reach statistical significance sooner and speed up the test?

How can you design variations that focus on maximizing lift for your primary goal?
A statistical calculation called the minimum detectable effect (MDE) can help you connect cost to your experiment design. Use it to make informed decisions about your experiment parameters.
You can also use MDE to prioritize tests and as part of your experimentation roadmap.
Using MDE
Minimum detectable effect (MDE) is a calculation that estimates the smallest improvement you’re willing to be able to detect. It determines how "sensitive" a test is.
Use MDE to estimate how long a test will take given the following:

Baseline conversion rate

Statistical significance

Traffic allocation
You can use Optimizely’s Sample Size Calculator to make this calculation.
For example, imagine these parameters:

Your baseline conversion rate is 15%

You'd like to measure statistical significance to 95%

You'd like to detect a 10% lift at minimum (this is your MDE)
According to the Sample Size Calculator, you’d need ~8,000 visitors per variation to reach statistical significance.
In reality, you don't know the actual lift in advance. If you did, you wouldn't be running the test, right? By estimating the minimum lift you'd like to detect, with a given level of certainty, you establish boundaries for how much traffic or time you'll invest in this experiment. You can plan and scope your test more accurately.
Let's follow the example above one step further.
You design the experiment above with four variations. Your site averages 10,000 unique visitors per week. If you show this experiment to 100% of visitors, it will probably take 3.2 weeks to reach significance.
8,000 visitors per variation x 4 variations = 32,000 visitors
32,000 visitors / 10,000 visitors per week = 3.2 weeks
At this stage, consider whether the traffic and time is worth it, and how you might design a faster test.
Best practices
Here are a few best practices for designing an experiment with MDE in mind.
Use potential business impact to decide on the sensitivity of your experiment. 
Use MDE as a guide rather than an exact prediction. 
Design impactful variations. 