Not reaching statistical significance?

Your A/B test finished and one variant had a higher open rate. But your ESP is flagging it as "not statistically significant." So who won? Honestly? Nobody yet.

Statistical significance is the math that tells you whether a difference you're seeing is real or just noise. If Variant A got 22% opens and Variant B got 24%, that 2-point gap might mean nothing at all. With a small enough sample, random luck can produce that kind of difference every time. Significance tells you when the result is big enough (and consistent enough) to trust.

Most ESPs express this as a confidence level. A 95% confidence level means there's only a 5% chance your result happened by random chance. Below that threshold, you don't have a winner. You have a coin flip with extra steps.

The common mistakes people make here:

• Calling the variant with the higher raw number the "winner" anyway
• Stopping the test early because the numbers "look good"
• Assuming two close results mean the variants are tied (they might just mean your sample is too small to detect the real difference)

What to actually do when you don't hit significance:

• Keep running the test if you can. More data usually helps, up to a point.
• Accept the result as inconclusive and move on. That's a valid outcome.
• Redesign the test with a bigger expected effect. If you tested a tiny subject line tweak, try a bolder change that's more likely to produce a clear signal.

Inconclusive isn't failure. It's information. It's telling you that whatever you tested probably doesn't matter much to this audience, or that you need a larger list before this kind of test will tell you anything useful. Acting on insignificant results isn't being decisive. It's guessing with extra paperwork.

One thing worth knowing: the more you test multiple variables at once, the harder it gets to reach significance on any of them. Keep tests focused on one change at a time.