Populations and Samples

Statistics · Axiom Academy

LESSON Populations and Samples Understanding the foundation of statistical inference: populations, samples, parameters, and statistics A population is the entire group of individuals or objects we want to study. A sample is a subset of the population that we actually observe and measure. 2. Parameters: Population Measures Parameters are numerical values that describe characteristics of a population. They are typically unknown and what we try to estimate. Key Point: Parameters are fixed values (though usually unknown). We use Greek letters to denote them: μ (mu) for mean, σ (sigma) for standard deviation. 3. Statistics: Sample Measures Statistics are numerical values calculated from sample data. They are used to estimate the corresponding population parameters. Key Point: Statistics vary from sample to sample. We use Roman letters: x̄ (x-bar) for sample mean, s for sample standard deviation. Statistical inference is the process of using sample statistics to estimate population parameters. Define the population of interest Select a representative sample Calculate statistics from the sample Use statistics to make inferences about parameters

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