Statistical Significance & Avoiding False Positives
Learn the statistics behind trustworthy experiments: p-values, sample size, statistical power, and the traps that produce false wins in growth testing.
Statistical Significance & Avoiding False Positives is a free ASO & App Growth lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the ASO & App Growth learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
When Is a Win a Real Win?
Growth teams run constant experiments—but acting on false positives wastes effort and misleads strategy. Understanding statistical significance separates real wins from noise.
This lesson gives you the statistical literacy to trust your results.
Variation Is Everywhere
Even identical groups produce different numbers by chance. The core question of A/B testing is: is the observed difference real, or just random variation?
Statistics answers this rigorously.
Understanding the p-value
A p-value is the probability of seeing a result at least this extreme if there were truly no difference. A common threshold is p < 0.05.
p = 0.03 -> if there were no real effect,
only 3% chance of this result
-> usually deemed significant
p = 0.30 -> likely just noiseWhat Significance Does NOT Mean
p < 0.05 does not mean a 95% chance the hypothesis is true, nor that the effect is large. It only controls how often you wrongly cry "win" when nothing happened.
Statistical significance is not the same as business significance.
Sample Size Matters
Small samples produce wild swings and unreliable p-values. You need enough users per variant to detect the effect you care about.
Small sample: B looks +30%... then reverts
Large sample: stable, trustworthy estimate
Decide required sample size BEFORE launching.Statistical Power
Power is the chance of detecting a real effect if one exists (commonly targeted at 80%). Underpowered tests miss true winners—a false negative.
Power depends on sample size and effect size.
Minimum Detectable Effect
Before testing, set the minimum detectable effect (MDE)—the smallest improvement worth detecting. Smaller MDEs require larger samples.
Want to detect a 2% lift? -> need big sample
Only care about >10% lift? -> smaller sample okThe Peeking Problem
Checking results repeatedly and stopping the moment p < 0.05 dramatically inflates false positives. This peeking is one of the most common growth-testing mistakes.
Commit to a sample size and wait.
Multiple Comparisons
Test 20 variants at p < 0.05 and roughly one will look significant by pure chance. Testing many things at once inflates false positives—correct for it or pre-register your main metric.
Confidence Intervals Tell More
A confidence interval shows the plausible range of the true effect, not just a yes/no verdict. A wide interval crossing zero means you cannot be confident of direction—even with a tempting point estimate.
Building Trustworthy Experiments
A reliable test: define one primary metric, set MDE and sample size up front, run to completion without peeking, then judge on significance and practical impact.
Quick Check
Test your understanding of experiment statistics.
Recap: Significance & False Positives
You learned to run trustworthy experiments:
- A p-value measures the chance of a result under no real effect.
- Significance is not the same as a large or guaranteed-true effect.
- Set sample size, power, and MDE before launching.
- Avoid peeking and correct for multiple comparisons.
Frequently asked questions
Is the “Statistical Significance & Avoiding False Positives” lesson free?
Yes — the full text of “Statistical Significance & Avoiding False Positives” is free to read here on the web, and the ASO & App Growth course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the ASO & App Growth course, upgrade to CoddyKit PRO.
What will I learn in “Statistical Significance & Avoiding False Positives”?
Learn the statistics behind trustworthy experiments: p-values, sample size, statistical power, and the traps that produce false wins in growth testing. You practise ASO & App Growth with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start ASO & App Growth?
No prior experience is required. ASO & App Growth on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Statistical Significance & Avoiding False Positives” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this ASO & App Growth lesson?
Yes. Every ASO & App Growth lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Developing a Growth Hypothesis & Framework
- Running A/B Tests Across Growth Channels
- Iterating & Scaling Successful Experiments
- Statistical Significance & Avoiding False Positives