Type I Error
A false positive in hypothesis testing - incorrectly rejecting the null hypothesis and concluding that a change had a real effect when the observed difference was actually due to random chance.
Also known as: false positive, alpha error
Formula
1 - (1 - alpha)^N
Why type i error matters
A Type I error means you ship a change that does not actually improve anything - or might even make things worse. You celebrate a winning experiment, roll out the variant, and wonder months later why the improvement never materialized in your overall metrics.
The cost of Type I errors extends beyond the immediate bad decision. They erode trust in the experimentation process. After a few "winning" tests that do not deliver real results, stakeholders start questioning whether experimentation is worthwhile. This chilling effect can be more damaging than any single false positive.
The probability of a Type I error is controlled by your significance level (alpha), which is the complement of your confidence level. At 95% confidence (alpha = 0.05), you accept a 5% chance of a false positive per test. If you run 20 tests, you should expect roughly one false positive by chance alone, even if none of the changes had any real effect.
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How to calculate type i error
The probability of a Type I error equals your significance level (alpha). At 95% confidence, alpha = 0.05, meaning a 5% chance of false positive per test. When running multiple tests, the family-wise error rate increases: for N independent tests, the probability of at least one false positive is 1 - (1 - alpha)^N.
Multiple Test False Positive Probability Calculator
1 - (1 - alpha)^N
Type I Error examples
A retailer runs 15 A/B tests per month and notices that "winning" tests rarely show lasting improvement. Analysis reveals they are peeking at results daily and calling winners early, producing a 30%+ false positive rate. Implementing fixed test durations reduces false discoveries to under 5%.
A product team tests a new pricing page with four variants against the control. Without correction, each comparison uses alpha = 0.05, giving a 19% chance of at least one false positive. They apply Bonferroni correction (alpha = 0.0125 per comparison) to maintain the overall 5% error rate.
How to Track in KISSmetrics
Control Type I error rates by setting appropriate significance levels in KISSmetrics experiments and resisting the temptation to peek at results early. When running multiple tests, apply corrections like Bonferroni (divide alpha by the number of tests) or use false discovery rate control methods.
Common Mistakes
- -Ignoring the multiple testing problem when running many simultaneous experiments
- -Peeking at results repeatedly during a test, which dramatically inflates the false positive rate
- -Setting significance levels too loosely (alpha = 0.10) for high-stakes decisions
- -Not tracking the historical false positive rate of your experimentation program
Pro Tips
- +Track how often "winning" experiments actually improve metrics post-rollout - if the hit rate is below 80%, your false positive rate may be too high
- +Use sequential testing methods (like alpha spending functions) if you need to monitor results during the test
- +Apply Bonferroni correction when testing multiple variants or metrics: divide your alpha by the number of comparisons
- +Consider the asymmetric cost of errors - if a false positive costs more than a false negative, lower your alpha
Related Terms
Type II Error
A false negative in hypothesis testing - failing to reject the null hypothesis and concluding that a change had no effect when it actually did produce a real improvement.
P-Value
A p-value is the probability of seeing a result at least as extreme as the one you measured, assuming the null hypothesis of no real difference is true. You calculate it by converting the observed difference into a test statistic such as a z-score, then reading the tail area beyond it. A p-value below 0.05 is the conventional threshold for calling a result statistically significant.
Null Hypothesis
The default assumption in a statistical test that there is no meaningful difference between the control and test groups - any observed difference is due to random chance rather than a real effect.
Confidence Level
The percentage probability that a confidence interval calculated from a given experiment will contain the true population parameter, commonly set at 90%, 95%, or 99% in A/B testing.
Statistical Power
The probability that a test will correctly detect a real effect when one exists, typically set at 80% as a minimum standard. Higher power means a lower chance of missing genuine improvements.
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