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Analysis and Evaluation RESOURCES I PRIMT I HELP

Sample Size and Confidence Levels

“Sample size” refers the number of times a specific test event is run . I t is a critical parameter in the testing process. Obviously, the more times a specific test event is performed, the higher the confidence level will likely be in terms of accuracy when test results are evaluated.

However, while increasing test sample sizes lowers the risk of inaccurate evaluations, it also directly increases testing costs and lengthens schedules. There is a point of decreasing returns where increasing sample size does not appreciably reduce risks . Part of good test planning involves using statistics to aid in the selection of an appropriate sample size for later testing that achieves a reasonable balance between evaluation accuracy and testing affordability.

Variation in test result data is a natural consequence the testing process. Excessive variation in test results, especially “outliers” (data far outside the expected curve of results ), indicates potential system performance anomalies (design flaws) or a poorly run test event.

The smaller the variance in the test data, the more confident we can be in the conclusions .

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TST102 Fundamentals of Test Evaluation

Lesson 19 – Analysis and Evaluation RESOURCES I PRIMT I HELP

Sample Size and Confidence Levels

“Sample size” refers the number of times a specific test event is run . It is a critical parameter in the testing process. Obviously, the more times a specific test event is performed, the higher the confidence level will likely be in terms of accuracy when test results are evaluated.

However, while increasing test sample sizes lowers the risk of inaccurate evaluations, it also directly increases testing costs and lengthens schedules. There is a point of decreasing returns where increasing sample size does not appreciably reduce risks . Part of good test planning involves using statistics to aid in the selection of an appropr sonable balance between evaluation accuracy and te Decreasing Returns

Variation in test result date As a practical example, check out the trusty ssive variation in test results, especially “outliers” performance anomalies (de

monograph of the binomial distribution available in Resources. You’ll see that, for a given reliability level (say 90% ), for one failure,

~i cates potential system

The smaller the variance in increasing the sample size from 40 to 70 only conclusions. changes the confidence level from 0.95 to 0.995, an increase of just 0.045 but at the expense of nearly twice as many test articles!