Knowra Sample size determination Sample size determination Sample size determination is the process of choosing how many observations or participants a study needs to meet specified goals for precision, statistical power, or representativeness.
Statistical power : The probability that a statistical test rejects a false null hypothesis under specified conditions. Power-based calculations set sample size to make detecting a target effect sufficiently likely.
Sampling distribution : The probability distribution of a statistic across repeated samples drawn under the same conditions. Sample size calculations often use how estimator uncertainty changes across repeated samples.
Power analysis : A statistical method for relating sample size, effect size, significance level, and probability of detection. It is the standard framework for planning samples around a target chance of detecting an effect.
Post hoc power analysis : A calculation of statistical power using an observed effect estimate after data collection. It differs from prospective planning and often adds little beyond the result's uncertainty.
Pilot study : A small preliminary study used to assess procedures, feasibility, or inputs for a later investigation. Pilot estimates can inform planning, though their imprecision can make direct reuse risky.
Effect size : A quantitative measure of the magnitude of a difference, association, or other phenomenon. Smaller target effects generally require larger samples to detect.
Standard error : The standard deviation of a statistic's sampling distribution. It connects sample size to the expected variability of estimates.
Survey sampling : The design and analysis of methods for collecting data from a subset of a population. Survey sample sizes must account for population estimates, selection design, and nonresponse.
Minimum detectable effect : The smallest effect a study can detect at specified error rates and sample size with a chosen power. Planning can solve for the detectable effect instead of choosing sample size for a fixed target.
Missing data : Data values absent for some units or variables in a study. Expected missingness can reduce effective information and complicate enrollment targets.
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