What is Statistical Significance?
Statistical significance is a way to judge whether an observed difference or relationship in data is likely to be real rather than a result of random chance. It uses probability thresholds to help decide whether to trust an apparent pattern in survey results.
Statistical significance is a concept from inferential statistics that helps you determine whether a difference (for example, between two groups' responses) or a relationship (for example, between age and satisfaction) is likely to reflect a real effect in the population rather than random variation in your sample. Analysts typically start with a null hypothesis (no difference or no effect) and calculate a probability (a p-value) that the observed result would occur if the null hypothesis were true. If that probability is low enough—commonly below 0.05—the result is called statistically significant.
Important caveats: statistical significance does not measure how large or important an effect is, it depends on sample size, and it assumes the data and methods meet certain conditions (representative sampling, appropriate test choice, etc.).
Usage example
A council runs the same satisfaction survey in English and Somali. English respondents rate satisfaction 62% and Somali respondents 50%, with sample sizes of 400 and 120 respectively. A statistical test returns p = 0.02, which is below the common 0.05 threshold, so the difference is statistically significant — meaning it’s unlikely to be caused by random sampling alone. The council still checks effect size, confidence intervals and whether the samples are comparable before acting on the result.
Practical application
For Hearo users, statistical significance helps decide whether observed differences between languages, locations or demographic groups are likely meaningful or just noise. That matters when prioritising follow-up actions (e.g., targeted communications, policy changes, or further investigation), when reporting findings to stakeholders, and when designing surveys (sample size and question clarity affect the ability to detect real differences). Use significance together with effect size, confidence intervals and practical judgment: a statistically significant but tiny difference may not justify major changes, while a large but not-yet-significant pattern in a small sample might warrant more data collection.
FAQ
Does a statistically significant result always mean the difference is important?
No. Statistical significance only says a result is unlikely to be due to chance; it doesn’t speak to practical importance. Always look at effect size (how big the difference is), confidence intervals, and whether the difference matters for decisions or services.
What p-value or threshold should I use?
0.05 (5%) is a common convention, but thresholds are context-dependent. For high-stakes decisions you might require stronger evidence (e.g., 0.01). Also report confidence intervals and raw numbers rather than relying solely on a single threshold.
How does sample size affect statistical significance?
Larger samples make it easier to detect small differences (they can become statistically significant even if tiny), while small samples may fail to detect real but moderate effects. Plan sample sizes to have enough statistical power for the differences you care about.
Can translation or wording issues change whether a result is statistically significant?
Yes. Poorly translated or unclear questions can bias responses, change who completes the survey, or increase random noise — all of which affect statistical tests. Using clear multilingual surveys and allowing participants to flag problematic wording (as Hearo does) reduces that risk.