What does 'statistical significance' indicate in research findings?

Statistical significance indicates the likelihood that the observed results in a study are not due to chance, but rather reflect a true effect or relationship in the data being analyzed. When researchers calculate statistical significance, they often use a p-value to determine if the findings fall below a predetermined threshold (commonly 0.05), suggesting that the probability of the results occurring by random variation alone is very low. This concept is crucial in research because it helps to validate the findings and provides a level of confidence that the results can be attributed to the variables being studied rather than random error.

The other options do not accurately reflect the meaning of statistical significance. For example, accepting results by all researchers involves consensus and peer validation beyond just statistical calculations, while universal application pertains to the generalizability of findings across different contexts, which is not what statistical significance measures. Additionally, false claims about data relate to issues of integrity and accuracy in research reporting, which is separate from the statistical analysis that determines significance.