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Sample Size Calculations - Coggle Diagram
Sample Size Calculations
Importance of Sample Size
Helps reduce uncertainty in conclusions about a population
Essential for accurate inferential statistics
Questions for Setting Alpha, Beta, and Delta Values
Determine the smallest practical difference relevant to the business
Consider costs and risks associated with errors in decision-making
Type I and Type II Errors
Type II: Incorrectly accepting a false null hypothesis
Risks denoted by alpha (Type I) and beta (Type II)
Type I: Incorrectly rejecting a true null hypothesis
Analysis of Sample Size Tests in Minitab
Interpretation of results to make informed decisions about sample size
Step-by-step process for running and analyzing sample size tests
Guidelines for Setting Various Numbers
Adjustments based on the type of hypothesis test and data
Baseline values for alpha, beta, and delta based on business needs
Sample Size Calculation Methods
Use of software like Minitab for calculations
Different methods for various hypothesis tests (e.g., 1-Sample T Test, 2-Sample T Test, 1-Sample Proportion Test).
Considerations for Random Sampling
Caution against drawing wrong inferences due to non-random sampling
Emphasize the importance of random sampling for accurate results
Confidence Interval (Margin of Error)
Determines the range within which the true population parameter is likely to fall
Represents the uncertainty associated with sampling
Factors for Choosing Sample Size
Delta (practical difference to detect)
Standard deviation of the population
Beta level (determined based on acceptable Type II error risk).
Type of data (discrete or continuous) and type of test
Alpha level (usually set at 0.05)