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Statistical testing in psychology is essential for validating experimental findings and ensuring robust scientific conclusions. It involves evaluating if effects are significant or by chance, using parametric tests for normally distributed data and non-parametric tests for skewed data. Understanding these tests and their interpretations, including the risks of Type 1 and Type 2 errors, is crucial for researchers.
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Statistical testing provides a framework for determining the validity of experimental findings in psychology
Statistical testing allows researchers to make objective decisions about the validity of their hypotheses
The use of statistical testing in psychology ensures that conclusions are not based on chance or random occurrences
Parametric tests assume a normal distribution of data and are used for continuous data that meets certain assumptions
Non-parametric tests are used when data does not conform to the assumptions of parametric tests
Examples of parametric tests include correlation tests, t-tests, and ANOVA, while non-parametric tests include the Friedman test, Spearman's rank correlation, Wilcoxon signed-rank test, and Mann-Whitney U test
The significance of statistical tests is determined by the p-value, with a lower value indicating a significant effect
Researchers must be aware of the risks of Type 1 and Type 2 errors when interpreting the results of statistical tests
A thorough understanding of statistical tests, their assumptions, and limitations is crucial for drawing reliable conclusions in psychological research
00
Parametric tests assume ______ distribution, while non-parametric tests do not, making them fit for data that is ______.
normal
non-normal
01
Role of hypothesis testing in research
Determines if findings reflect experimental conditions or chance.
02
Impact of chance on research results
Statistical tests calculate probability results are due to randomness.
