What is statistical conclusion validity in research?

A brief introduction to statistical conclusion validity

For more best practices see our method overview

Content

Definition of statistical conclusion validity.

Statistical conclusion validity is “the validity of inferences about the correlation (covariation) between treatment and outcome” (Shadish et al., 2002). Statistical conclusion validity is closely related to and, in the case of quantitative experiments, a necessary condition for internal validity, requiring the correct use of statistics to infer a statistically significant correlation between the independent and dependent variable.

The other dimensions of research rigor that correspond to statistical conclusion validity are:

On our page on rigor you can get an overview for judging whether these are the dimensions to evaluate for your research or if (for qualitative research) you should use framework of trustworthiness instead.

Threats to statistical conclusion validity

The following table presents the most common threats to statistical conclusion validity and their consequences if not mitigated.

Threat Low statistical power	Consequence Non-significance of inference may be incorrectly assumed.
Threat Unreliability of treatment implementation	Consequence Effects may be underestimated if treatment is not fully implemented equally for all participants.
Threat Extraneous variance in the experimental setting	Consequence May introduce confounding factors.
Threat Inaccurate effect size estimation	Consequence Measurements may be skewed due to outliers.

Strategies to improve statistical conclusion validity.

Appropriate Sample Size: Ensure that the sample size is large enough to detect meaningful effects. Power analysis can help determine the required sample size for a given effect size and significance level.
Random Sampling: Use random sampling techniques to create a representative sample that accurately reflects the characteristics of the population.
Reliable Measurements: Employ reliable and valid measurement instruments to minimize measurement errors. Pilot testing can help identify and rectify measurement issues. This also helps your reliability
Assumption Checking: Verify the assumptions of chosen statistical tests. If assumptions are violated, consider using alternative methods or transformation techniques.
Transparent Reporting: Clearly document the methods and procedures used in the study, allowing others to replicate and verify your findings.

Conclusion on statistical conclusion validity.

In the pursuit of scientific knowledge, statistical conclusion validity plays a pivotal role in ensuring that your research findings are robust and valid. By addressing threats to validity and employing sound strategies, you can enhance the accuracy of your conclusions. This, in turn, contributes to the cumulative progress of science and the development of accurate theories and practical applications.

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