Been running tests for months but honestly wondering if I’m just fooling myself with the data.
Sample sizes feel too small, test duration keeps changing based on business needs, and I’m cherry-picking metrics that look good.
How do you actually know your tests mean something?
Your gut’s right about cherry picking.
I quit tracking multiple metrics because it’s a mess. Now I watch one revenue metric and ignore the rest during tests.
Changing test duration screws you every time. Business pressure is real, but it makes your data useless.
Just ship it and see what happens with revenue.
Small samples happen all the time. Just run fewer tests and focus on what will actually make a difference.
Been there too many times. Sample size kills me - now I just pause campaigns when volume’s too low rather than run with garbage data.
What saved me: I write my hypothesis and success metric before starting. Something like “New onboarding boosts day 7 retention by 5%.” Then I don’t get distracted by shiny metrics that pop up mid-test.
Business pressure to kill tests early never stops. I show stakeholders the timeline upfront and get them to agree. Cuts out those awkward “can we call it yet?” conversations.
This video nails the statistical traps you’re hitting:
My early tests were trash because I didn’t know about multiple comparisons or why peeking at results screws everything up.
You’re hitting the usual testing mistakes that kill results. Pick one metric upfront - revenue per user OR conversion rate, not everything at once. Always run tests in full week chunks since business cycles will screw up your data otherwise. Use a sample size calculator instead of guessing. If you need 10k users per variant but only have 2k, either wait it out or test something with bigger impact. Here’s the hard part - stop when you hit statistical significance, even if the results suck. It’s the only way your process stays reliable.