Is the difference in your results real — or just noise
Compare two scores or percentages and find out whether the gap between them is statistically significant, in seconds.
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What is statistical significance?
Statistical significance tells you whether the difference between two results is likely real or just due to random chance. In consumer research, that means knowing whether a gap in purchase intent, liking scores, or preference percentages reflects a genuine difference — not sampling noise.
How to test your results for significance
Choose the test that matches your data, then enter your numbers below for an instant read.
Compare two percentages — like 72% purchase intent for Concept A versus 64% for Concept B. Built for survey results and concept test scores.
Compare two average scores — like an overall liking of 7.2 versus 6.8 on a 9-point scale. Built for sensory testing and product ratings.
How sure you want to be before calling a difference real. Most researchers use 95%; exploratory work often uses 90%, high-stakes decisions 99%.
Try it yourself
Choose your test type, enter your two results, and get an instant read on significance.
Common significance-testing mistakes to avoid
A statistically significant difference tells you it’s real — not that it’s large enough to matter for your decision.
Small samples rarely reach significance even when a real difference exists — plan for at least 100 per group for most proportion tests, 30+ for mean comparisons.
A result that isn’t significant doesn’t mean there’s no difference — just that your current data can’t prove one yet.
Get the data worth testing
A significance test is only as good as the data behind it. Highlight’s end-to-end testing gives you real consumer scores to put into the calculator.
- Recruit the right respondents for your concept or sensory test
- Collect clean, verified survey and rating data
- Get results fast enough to test, learn, and iterate before launch
Common statistical significance calculator questions
What does a p-value of 0.05 mean?
There’s a 5% probability that the observed difference happened by chance alone — the standard threshold for calling a result statistically significant.
What’s the difference between a z-test and a t-test?
A z-test compares two percentages, like purchase intent or top-box scores. A t-test compares two average scores, like overall liking on a rating scale.
How many respondents do I need?
At least 100 respondents per group for most proportion tests, and 30 or more per group for mean comparisons — fewer, and even a real difference may not reach significance.
Can I use this for A/B testing?
Yes, but it’s optimized for product research scenarios — concept, sensory, and claims testing — where sample sizes are typically smaller than website A/B tests.
What if my result is not significant?
It doesn’t mean there’s no difference — just that your current data can’t prove one. A larger sample or a clearer difference may still reach significance.
Test with confidence, not guesswork
Get the real consumer data behind every result — book a session and see how Highlight powers decision-ready research.