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Casual Articles - Test Campaign Result Accuracy Test Group Sizing Part II
When a good employee starts performing poorly, it may be something outside the workplace that is causing his performance to suffer. Is his marriage in trouble? Does he have crushing debt? Did a parent recently die? You will learn this only if
Most industrial plants in need of oil analysis services might begin their search on the web. While this is a common and effective place to begin the evaluation process, it definitely will not tell the whole story. Knowing the right questions to
If the test campaign has been run on a group of size N and the response rate measured was p, the standard deviation is calculated by the following formula SEP = SQRT(p*(1-p) /N) . For example if the group size N has been 60 thousand and the response rate p was 4 %, then the standard deviation (SEP) is 0,08%. This means that one can be 68% confident that the response rate will range between 3,92% and 4,08% (within one standard deviation) or 95% confident that it will range between 3,84% and 4,16%. (the confidence level of 95% is the probability to fall within the response rate range and is found approximately 2 standard deviations from the mean).
As can be understood by the formula above, the larger the size N of the group, the smaller the standard deviation and the narrower the confidence internal. Using larger groups (larger samples) leads to higher confidence in the evaluation made. Up to this point, we have discussed the case of a single test group.
What if a test campaign aims at the comparative evaluation of two alternative customer selection models in order to identify the model which has higher predictive power. In that case, the test yields two response rates of the two test groups used. If the response rates differ substantially than the conclusion is clear. However if the two results are located close to each other, then the confidence intervals may overlap, thus leading to no clear conclusion. In that case, one should calculate the confidence interval of each group
People are the most important resources of an organization. They ensure the interaction of financial, industrial, and other resources so that the organization can function. Nowadays experienced managers realize that he financial reward cannot sta
If the test campaign has been run on a group of size N and the response rate measured was p, the standard deviation is calculated by the following formula SEP = SQRT(p*(1-p) /N) . For example if the group size N has been 60 thousand and the response rate p was 4 %, then the standard deviation (SEP) is 0,08%. This means that one can be 68% confident that the response rate will range between 3,92% and 4,08% (within one standard deviation) or 95% confident that it will range between 3,84% and 4,16%. (the confidence level of 95% is the probability to fall within the response rate range and is found approximately 2 standard deviations from the mean).
As can be understood by the formula above, the larger the size N of the group, the smaller the standard deviation and the narrower the confidence internal. Using larger groups (larger samples) leads to higher confidence in the evaluation made. Up to this point, we have discussed the case of a single test group.
What if a test campaign aims at the comparative evaluation of two alternative customer selection models in order to identify the model which has higher predictive power. In that case, the test yields two response rates of the two test groups used. If the response rates differ substantially than the conclusion is clear. However if the two results are located close to each other, then the confidence intervals may overlap, thus leading to no clear conclusion. In that case, one should calculate the confidence interval of each group
As management members and business owners we detest dealing with unions in our businesses. Unfortunately, the government has allowed people to collectively bargain for compensation & wages, benefits and terms of employment. This leaves many compa
As can be understood by the formula above, the larger the size N of the group, the smaller the standard deviation and the narrower the confidence internal. Using larger groups (larger samples) leads to higher confidence in the evaluation made. Up to this point, we have discussed the case of a single test group.
What if a test campaign aims at the comparative evaluation of two alternative customer selection models in order to identify the model which has higher predictive power. In that case, the test yields two response rates of the two test groups used. If the response rates differ substantially than the conclusion is clear. However if the two results are located close to each other, then the confidence intervals may overlap, thus leading to no clear conclusion. In that case, one should calculate the confidence interval of each group
My friend Craig bought an X-Box 360 last month. For those not familiar with what an X-Box is, its a video game console. Most of us old enough to remember, would compare it to a suped-up Atari. Well, if Atari were a Pinto, the X-Box 360 would
What if a test campaign aims at the comparative evaluation of two alternative customer selection models in order to identify the model which has higher predictive power. In that case, the test yields two response rates of the two test groups used. If the response rates differ substantially than the conclusion is clear. However if the two results are located close to each other, then the confidence intervals may overlap, thus leading to no clear conclusion. In that case, one should calculate the confidence interval of each group
Is the customer always right? How far should a company go to satisfy their clientele or customer base? Is there a point when satisfying the customer is actually harmful to the enterprise or as the saying goes, is the customer always right? In tod
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