Inferential Statistics

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A population is a group of all distinct individuals or objects that you want to draw conclusions about. For example, all students enrolled in a school when studying student exercise habits, or all com

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A sample is a small subset of a larger population selected for making inferences about the entire population. Key points: 1) Sample size is always smaller than population size, 2) Used to estimate pop

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Simple random sampling is a method where every individual is chosen entirely by chance and every member of the population has an equal chance of being included in the sample. Example: From a class of

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Systematic random sampling involves choosing members at regular intervals from a population. Steps: 1) Arrange population in order, 2) Select starting point randomly, 3) Choose every nth item. Example

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A representative sample accurately reflects the features of the population. Conditions: 1) Must have random selection component, 2) Sample size must be large enough to show population variability. Exa

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Sampling error is the difference between a population parameter and a sample statistic. Formula: Sampling error = x̄ - μ, where x̄ = sample mean and μ = population mean. It occurs because random sampl

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Parameter: 1) Characteristic of population, 2) Calculated from entire population, 3) Uses Greek letters (μ, σ, σ²). Statistic: 1) Characteristic of sample, 2) Calculated from sample observations only,

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Statistical significance is a measure of reliability of findings that establishes confidence that a result is real and the sample was framed wisely. It is determined through statistical hypothesis tes