If a relationship has practical significance, does it guarantee that statistical significance will be achieved in every study that examines it? Yes No Explain. (Select all that apply.) In some studies there may be an unfortunate "luck of the draw" in that sample results may not be consistent with the truth in the population. If the sample size of a study is small, there may not be enough information to declare statistical significance. If the sample size of a study is large, confounding variables may be introduced that cause the sample results to not be statistically significant. If the relationship is real, it should be represented in all properly selected random samples.

Option B - If the sample size of a study is small, there may not be enough information to declare statistical significance.

Step-by-step explanation:

No.

This is a variation in the absence of a relationship between causality and correlation. The missing value is risk perception. A relationship with a practical significance (risk or benefit) as perceived by an individual may not show "statistical significance" based on the chosen risk factors or confidence intervals.

Statistical significance does not imply both causality and practical significance. Statistics are expected to guide decisions, but they are not the only valid basis for decisions.

Therefore, if the sample size of a study is small, there may not be enough information to declare statistical significance.