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1 year ago in Research Methods , Statistical Analysis By Kanishka

Understanding non-random sampling and statistical significance

When dealing with non-random sampling, how does it affect the assessment of statistical significance in research studies, and what considerations should researchers keep in mind when interpreting the results of statistical tests?

ResearchObjectives StatisticalTests Statistical Analysis Statistics

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By Ria Desai Answered 1 year ago

Non-random sampling refers to the deliberate selection of participants or data points based on specific criteria rather than random selection from the population of interest. When analyzing data from non-random samples, researchers should be cautious when interpreting statistical significance, as the results may not generalize to the broader population. Statistical tests such as t-Student or Mann-Whitney tests with enormous sample sizes may yield significant results due to large sample effects, but caution is warranted to ensure the practical significance of the findings. Researchers should consider the representativeness of the sample, potential biases, and the relevance of the research question when interpreting statistical significance in non-random sampling contexts.

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