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Introduction to Statistics01:17

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The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
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Understanding statistical populations and inferences.

Jean Raymond1, Tim E Darsaut2

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Summary

The term "population" in clinical research is confusing and often misused, leading to errors in study design and interpretation. Researchers should use this term sparingly to avoid misconceptions about generalizations and inferences.

Keywords:
Causality, pseudo-populationsClinical trialsMethodologyPropensity scoresSuper-populations

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Area of Science:

  • Clinical Research
  • Biostatistics
  • Epidemiology

Background:

  • The term 'population' in clinical research and statistics is often ambiguous and multifaceted.
  • Misapplication of the term 'population' can result in significant errors in study design, analysis, and interpretation.

Purpose of the Study:

  • To review diverse concepts of populations.
  • To examine the relationship between populations and statistical inferences.
  • To clarify whether populations refer to individuals, variables, or theoretical constructs.

Main Methods:

  • Review of different notions of populations.
  • Analysis of their connection to statistical inference methods.
  • Exploration of their application to persons, variables, and theoretical constructs.

Main Results:

  • Distinction between design-based and model-based statistical inferences.
  • Populations rarely refer to actual patients in clinical research.
  • Super-populations, pseudo-populations, and statistical populations represent theoretical or mathematical constructs, leading to analytical complexity.
  • Target populations are often erroneously equated with eligibility criteria when a real population is absent.
  • The inductive problem of generalizing from study subjects to future patients remains unresolved due to semantic ambiguity.

Conclusions:

  • The term 'population' frequently obscures rather than clarifies issues related to generalization and inference in clinical research.
  • Due to its potential for errors and misconceptions, the use of the term 'population' should be minimized in clinical research.