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

2. 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.

3. 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 =
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 =
= 1538

4. Data to Examine Documents Qualitative Documents Reports and decisions
Performance reports
Production
Quality
Workers
Data capture forms
Orders
Messages
Feedback
Work records
Qualitative Documents
Memos
Notices
Manuals