Power | Effect size | Sample size in R | Null hypothesis | Type II error | Significance level

In designing an experiment, a key question is:
How many animals/subjects do I need for my experiment?
• Too small of a sample size can under detect the effect of interest in your experiment
• Too large of a sample size may lead to unnecessary wasting of resources and animals
• Like Goldilocks, we want our sample size to be ‘just right’
• The answer: Sample Size Calculation
• Goal: We strive to have enough samples to reasonably detect an effect if it really is there without wasting limited resources on too many samples.
Effect size: magnitude of the effect under the alternative hypothesis
• The larger the effect size, the easier it is to detect an effect and require fewer samples
Power: probability of correctly rejecting the null hypothesis if it is false
• AKA, probability of detecting a true difference when it exists
• Power = 1-β, where β is the probability of a Type II error (false negative)
• The higher the power, the more likely it is to detect an effect if it is present and the more samples needed
• Standard setting for power is 0.80
Significance level (α): probability of falsely rejecting the null hypothesis even though it is true
• AKA, probability of a Type I error (false positive)
• The lower the significance level, the more likely it is to avoid a false positive and the more samples needed
• Standard setting for α is 0.05