Data Sampling Jerry Post Copyright © 1997 Chapter 4 Data Sampling Jerry Post Copyright © 1997

Data Sampling Samples: Cannot check every item Steps involved Hold costs down Reduce bias More accurate information Steps involved Determine data needed Identify population Choose sample and size Common techniques Simple random Stratified Sub groups Random sample from each group (strata). More complex Choose by multiple categories.

Sample Size Proportion Confidence Interval Example error = z * /n variance = p(1 - p) Solve for n: = p(1 - p) (z/error)2 + 1 Example Estimate p at 5% Confidence at 5% (z=1.96) Error interval = 0.02 Compute N = .05(.95) (1.96/0.02)2 + 1 = 457.19 N = 458 Raw data (non proportion) Confidence Interval error = z /n variance = 2x Solve for n: = (z/error)2 2x + 1 Example Estimate mean, std. dev., error Mean = $1500 Std. Dev. = $100 Error = $5 Compute N = (1.96/5)2 (100) 2 + 1 = 1537.64 = 1538

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