What's more general than a whole population?

Area of Science:

• Statistics
• Social Sciences
• Epidemiology

Background:

• Statistical inference traditionally excludes complete population studies due to lack of sampling variability.
• Recent studies on entire populations (e.g., national cancer cases) increasingly report inferential statistics.
• This practice raises concerns about the appropriateness of p values and confidence intervals in such contexts.

Purpose of the Study:

• To explore conditions where statistical inference is meaningful for complete population studies.
• To examine the theoretical underpinnings of applying inferential statistics to census-like data.
• To reconcile the use of statistical inference with the analysis of entire populations in social science research.

Main Methods:

• Literature review focusing on social science research methodologies.
• Conceptual analysis of statistical inference principles in relation to population studies.
• Examination of case studies and theoretical frameworks concerning generalizability.

Main Results:

• Statistical inference in complete population studies is meaningful only when an implicit, wider target population is defined.
• This target population may include future occurrences or broader geographical regions.
• The generalizability of the studied complete population to this wider target population is crucial for validity.

Conclusions:

• The application of statistical inference to complete population studies requires careful consideration of the intended target population.
• Researchers must justify the generalizability of their findings beyond the studied complete population.
• Clear definition of the target population is essential for the meaningful interpretation of inferential statistics in population-level studies.