What does it mean for a result to be statistically significant?

What does it mean for a result to be statistically significant?

• A. It is unlikely to have occurred by chance
• B. It shows a large effect size
• C. It is confirmed by previous studies
• D. It matches the assumptions of normality

Answer: (A)

A result is considered statistically significant when it is unlikely to have occurred by chance. This determination usually stems from a p-value calculated during hypothesis testing. If the p-value is lower than a predetermined significance level (commonly set at 0.05), it indicates that the observed data would be very rare if the null hypothesis were true. Therefore, researchers can conclude that there is sufficient evidence to suggest an effect or a difference that merits further investigation, rather than attributing the result to random variation alone. Other options may touch on various important aspects of statistical analysis but do not directly define statistical significance. For example, while a large effect size can indicate meaningfulness of a result, it does not intrinsically confirm that the findings are statistically significant. Similarly, results may align with previous studies or fit assumptions of normality without necessarily being statistically significant. It's vital to focus on the p-value context when discussing significance.

A result is considered statistically significant when it is unlikely to have occurred by chance. This determination usually stems from a p-value calculated during hypothesis testing. If the p-value is lower than a predetermined significance level (commonly set at 0.05), it indicates that the observed data would be very rare if the null hypothesis were true. Therefore, researchers can conclude that there is sufficient evidence to suggest an effect or a difference that merits further investigation, rather than attributing the result to random variation alone.

Other options may touch on various important aspects of statistical analysis but do not directly define statistical significance. For example, while a large effect size can indicate meaningfulness of a result, it does not intrinsically confirm that the findings are statistically significant. Similarly, results may align with previous studies or fit assumptions of normality without necessarily being statistically significant. It's vital to focus on the p-value context when discussing significance.