Testing Statistical Significance
Testing statistical significance is an excellent way to identify probably relevance between a total data set mean/sigma and a smaller sample data set mean/sigma, otherwise known as a population mean/sigma and sample data set mean/sigma. This classification of testing is also very useful in proving probable relevance between data samples. Although testing statistical significance is not a 100% fool proof, if testing to the 95% probability on two data sets the statistical probability is .25% chance that the results of the two samplings was due to chance. When testing at this level of probability and with a data set size that is big enough, a level of certainty can be created to help determine if further investigation is warranted. The following is a problem is used to illustrate how testing statistical significance paints a more descriptive picture of data set relationships. Sam Sleep researcher hypothesizes that people who are allowed to sleep for only four hours will score significantly lower than people who are allowed to sleep for eight hours on a management ability test. He brings sixteen participants into his sleep lab and randomly assigns them to one of two groups. In one group he has participants sleep for eight hours and in the other group he has them sleep for four. The next morning he administers the SMAT (Sam's Management Ability Test) to all participants. (Scores on the SMAT range from 1-9 with high scores representing better performance). Is Sam's hypothesis supported by this data?
SMAT scores 8 hours sleep group (X) 5 7 5 3 5 3 3 9 4 hours sleep group (Y) 8 1 4 6 6 4 1 2
When given a data set one of the most important evaluations is to determine if the data set size is big enough to show relevance. So, the first thing I did was to check if the size warranted further review. Finding the smallest relevant size of data is as simple as taking the confidence quotient and multiplying this by the standard deviation to the
SMAT scores 8 hours sleep group (X) 5 7 5 3 5 3 3 9 4 hours sleep group (Y) 8 1 4 6 6 4 1 2
When given a data set one of the most important evaluations is to determine if the data set size is big enough to show relevance. So, the first thing I did was to check if the size warranted further review. Finding the smallest relevant size of data is as simple as taking the confidence quotient and multiplying this by the standard deviation to the
References: Brussee, Warren (2004) Statistics for Six Sigma Made Easy, Publisher: McGraw-Hill. ISBN: 9780071433853 -----------------------