Market Research: Foundations to Practice · Analyzing and Interpreting Data
Statistical Significance and Practical Significance
A statistically significant difference and a practically meaningful one are not the same thing, and conflating them is one of the most consequential interpretation errors in applied research. This chapter covers the distinction.
Statistical significance testing evaluates whether an observed difference between groups (such as two survey segments, or two concept test scores) is unlikely to have occurred purely by random chance alone, typically expressed through a p-value, with a common convention treating a p-value below 0.05 as statistically significant, though this specific threshold is itself a widely used convention rather than any kind of universal, absolute law of nature.
- Statistical significance evaluates whether a difference is unlikely due to random chance, conventionally using a p-value below 0.05 as the threshold.
- With a large enough sample, even a practically trivial difference can become statistically significant, since larger samples detect smaller differences.
- A non-significant result doesn't prove no real difference exists; it may simply reflect an underpowered study with too small a sample to detect it.
- Effect size measures actual magnitude independent of sample size, letting stakeholders judge whether a significant difference is also large enough to matter.