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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
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Pervasive errors in hypothesis testing: Toward better statistical practice in nursing research.

Vincent S Staggs1

  • 1Biostatistics & Epidemiology Core, Health Services & Outcomes Research, Children's Mercy Kansas City, 2401 Gillham Rd., Kansas City, MO, USA; School of Medicine, University of Missouri-Kansas City, 2411 Holmes St., Kansas City, MO, USA.

International Journal of Nursing Studies
|July 27, 2019
PubMed
Summary
This summary is machine-generated.

Nursing researchers need a clearer understanding of statistical inference and hypothesis testing to avoid common errors. Improving statistical practices ensures the reliable interpretation of research findings and quantifies randomness effectively.

Keywords:
Nursing researchResearch methodsStatistical methodsStatistics

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Area of Science:

  • Nursing Research
  • Biostatistics
  • Quantitative Methods

Background:

  • Common statistical issues in nursing research include ignoring multiple testing, clinical significance, and effect sizes.
  • Subtle forms of multiple testing and researcher degrees of freedom are often overlooked.
  • These issues suggest a need for a clearer understanding of statistical inference for handling randomness.

Purpose of the Study:

  • To enhance the understanding and application of inferential statistics in nursing research.
  • To provide an accessible explanation of hypothesis testing and repeated sampling.
  • To examine common statistical errors and misconceptions in nursing research.

Main Methods:

  • Educational paper detailing hypothesis testing and repeated sampling.
  • Examination of statistical misconceptions: p-value misinterpretation, multiple testing in model selection, researcher degrees of freedom, and baseline differences.
  • Recommendations for improving statistical practices are provided.

Main Results:

  • Misinterpreting non-significant p-values as proof of the null hypothesis is a common error.
  • Failure to account for multiple testing during model selection and abuse of researcher degrees of freedom are prevalent issues.
  • Hypothesis testing for baseline differences in randomized trials requires careful consideration.

Conclusions:

  • Classical statistical inference methods based on repeated sampling remain essential for quantifying randomness in nursing research.
  • The hypothesis testing framework aids in ruling out chance as an explanation for observed effects.
  • Nursing researchers, reviewers, and educators must possess a strong understanding of statistical inference to improve research quality.