Which statement best describes statistical significance?

Which statement best describes statistical significance?

• A. It measures the practical importance of the result.
• B. It is unrelated to the alpha level.
• C. It depends on the sample size.
• D. It is a result unlikely to occur by chance alone, according to a pre-determined alpha level.

Answer: (D)

Statistical significance is about whether the observed result is unlikely to have happened by random chance, assuming there is no real effect (the null hypothesis), using a predefined threshold called alpha. The best description says a result is statistically significant if it would be unlikely to occur by chance alone, according to that predetermined alpha level. This captures the core idea: significance is a statement about the probability of the observed data under the null, not about how big the effect is in practical terms. It’s important to note the distinction from practical significance: a finding can be statistically significant yet have a trivial or unimportant real-world impact. And while the concept is defined with respect to alpha, in practice the chance of declaring significance can be influenced by sample size—larger samples can reveal statistically significant effects that are tiny in magnitude, whereas small samples might miss real effects.

Statistical significance is about whether the observed result is unlikely to have happened by random chance, assuming there is no real effect (the null hypothesis), using a predefined threshold called alpha. The best description says a result is statistically significant if it would be unlikely to occur by chance alone, according to that predetermined alpha level. This captures the core idea: significance is a statement about the probability of the observed data under the null, not about how big the effect is in practical terms.

It’s important to note the distinction from practical significance: a finding can be statistically significant yet have a trivial or unimportant real-world impact. And while the concept is defined with respect to alpha, in practice the chance of declaring significance can be influenced by sample size—larger samples can reveal statistically significant effects that are tiny in magnitude, whereas small samples might miss real effects.