minimum meaningful effect of biological relevance the larger the effect size, the smaller the experiment will need to be to detect it. This is to be determined scientifically, not statistically.
The power analysis depends on the relationship between 6 variables: the difference of biological interest the standard deviation the significance level the desired power of the experiment the sample size the alternative hypothesis (ie one or two-sided test) Effect sizeħ 1 The difference of biological interest Translate the hypothesis into statistical questions: What type of data? What statistical test ? What sample size? Very important: Difference between technical and biological replicates. Hypothesis Experimental design Choice of a Statistical test Power analysis: Sample size Experiment(s) Data exploration Statistical analysis of the resultsĥ Experimental design n=3 n=1 Think stats!! Main output of a power analysis: Estimation of an appropriate sample size Too big: waste of resources, Too small: may miss the effect (p>0.05)+ waste of resources, Grants: justification of sample size, Publications: reviewers ask for power calculation evidence, Home office: the 3 Rs: Replacement, Reduction and Refinement. Translation: the probability of detecting an effect, given that the effect is really there. Presentation on theme: "Introduction to Statistics with GraphPad Prism 7"- Presentation transcript:ġ Introduction to Statistics with GraphPad Prism 7Ģ Outline of the course Power analysis with G*Powerīasic structure of a GraphPad Prism project Analysis of qualitative data Chi-square test Analysis of quantitative data t-test, ANOVA, correlation and curve fittingģ Power analysis Definition of power: probability that a statistical test will reject a false null hypothesis (H0).
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