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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