Final answer:
Interactions are often more important than main effects in statistical analysis as they reveal how the effects of one factor depend on another.
Explanation:
False. In statistical analysis, interactions are often more important than main effects. An interaction effect occurs when the effect of one factor on the dependent variable differs depending on the level of another factor. When an interaction effect is present, interpreting main effects alone may not provide a complete understanding of the relationships in the data.
For example, if studying the effects of both dosage and age on patient outcomes, an interaction effect would mean that the impact of dosage on outcomes varies depending on the age of the patient. Therefore, considering interactions is crucial for a comprehensive analysis of the data.
Learn more about Interactions in statistical analysis here:
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