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.

Significance check

• Concept A: 72%
• Concept B: 64%
• Confidence: 95%
• 0.03 p-value — significant

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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.

Two tests, one answer

1. Proportions z-test
   Compare two percentages — like 72% purchase intent for Concept A versus 64% for Concept B. Built for survey results and concept test scores.

2. t-test on means
   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.

3. Confidence level
   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.

Statistical Significance Calculator

• Proportions (z-test)

• Means (t-test)

• Sample Size A:

• Proportion A (%):

• Sample Size B:

• Proportion B (%):

• Confidence Level:

Statistically Significant

• Test Statistic:
  z = 2.4254
• p-value:
  0.0114
• Confidence Level:
  95%

The 8-percentage-point difference (72% vs. 64%) is statistically significant at the 95% confidence level (p = 0.0114). This means we can be confident the difference reflects a real effect, not just random variation in the samples.

Common significance-testing mistakes to avoid

• Confusing significant with meaningful:
  A statistically significant difference tells you it’s real — not that it’s large enough to matter for your decision.

• Testing with too few respondents:
  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.

• Ignoring a non-significant result:
  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.