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Statistical tests

Non-parametric tests

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Delve into non-parametric test fundamentals for comparing two groups through varied scenarios in biomedical science and microbiology. Develop skills in defining hypotheses, validating data assumptions, selecting appropriate non-parametric tests, and interpreting results using the p-value.

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Learning outcomes

  • Define null and alternative hypotheses in the context of non-parametric tests.
  • Identify data distributions and data type to analyse with non-parametric tests.
  • Identify the appropriate non-parametric test based on study features and the choice between one-tailed or two-tailed analysis.
  • Interpret the results of a non-parametric test based on the p-value.
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Skills

Concepts, Principles and Theories

  • Identify the type of data being measured as the dependent variable

Manipulating and Presenting Data

  • Identify the appropriate type of non-parametric test to perform based on study features

Interpreting and Analysing Data

  • Examine the features of non-parametric tests for comparing two groups
  • Assess whether example data distributions are appropriate to analyse with non-parametric tests
  • Interpret the output and draw conclusions based on the results of a non-parametric test

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Prof. Dudley Shallcross
Professor of Atmospheric Chemistry, University of Bristol

“Students welcome the opportunity to repeat calculations with different data to test their understanding of the material. Student feedback on courses that I teach that use Smart Worksheets, always highlight this as a very positive aspect.”

Details

Degrees

Biomedical science
Biological science
Forensic science
Foundation science
Pharmaceutical science

Modules

Scientific literacy

Topic

Statistical tests

Purchase option

The Smart Worksheet Library

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Biomedical science
Biological science
Forensic science
Foundation science
Pharmaceutical science
non-parametric
hypothesis testing
Scientific literacy
The Smart Worksheet Library
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