provides a range of plausible values for population parameters, offering advantages over . It accounts for , provides precision measures, and offers confidence levels. This approach is crucial in decision-making across various fields, from to .
Constructing confidence intervals involves different formulas depending on the parameter being estimated and available information. is a critical aspect, considering factors like , acceptable , and population variability. Understanding these relationships helps balance precision and resource allocation in statistical decision-making.
Understanding Interval Estimation
Interval vs point estimation
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Interval estimation provides range of plausible values for population parameter expressed as
Point estimation gives single value as estimate of population parameter
Interval estimation advantages account for sampling variability, provide , offer in estimate
Applications in decision-making include (financial forecasting), quality control (manufacturing tolerances), market research (consumer preferences)
Construction of confidence intervals
Confidence interval for (known )
Formula: xˉ±zα/2nσ
Range containing true population mean with specified probability
Confidence interval for population mean (unknown standard deviation)
Uses
Formula: xˉ±tα/2,n−1ns
Confidence interval for population proportion
Formula: p^±zα/2np^(1−p^)
Used for binary data or percentages (voter preferences, product defects)