In the context of research, what does “statistical significance” refer to?

Statistical significance is a fundamental concept in research that indicates whether the results observed in a study are likely due to chance or represent a true effect in the population being studied. It is often determined using a p-value, which quantifies the probability of obtaining the observed results (or more extreme ones) given that the null hypothesis is true.

When researchers report that findings are statistically significant, they are asserting that the likelihood of observing such results if there were no actual effect is sufficiently low, often set at a threshold such as 0.05. This means that there is a less than 5% probability that the results occurred purely by chance, providing a basis for rejecting the null hypothesis.

The other options, while related to research, do not accurately capture the essence of statistical significance. The degree to which results can be trusted relates more to concepts like validity and reliability rather than statistical significance itself. The relationship strength between variables speaks to effect size, not the probability of being due to chance. Lastly, the accuracy of data collection techniques pertains to the methods employed in gathering data rather than the interpretation of the results' significance.