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1. The Process Of Using Statistical Analysis Hypothesis Testing - Professional Services™ In any judgment, we believe that there would be always a probability error and it is up to us that how this error contributes to our judgment, normally in 100% percent judgment 95% confidence level, and 5% error. if the condition is something like that so most of the cases our judgment is true but if an error is more than 5% than judgment could be wrong it is up to us that up to which percentage of error we accept the judgment according to criticality. In the hypothesis testing, we always doubt that current trend is not ok and then with the hypothesis testing proves whether the assumption made by us is true or not Ex: (1) Data of the population is normal Our doubt that data is not normal and start an analysis If P value equal to or greater than .05 than null If P value less than .05 than alternate Let's say P value comes greater than .05 than there is null hypothesis and our doubt is not true In the above example condition given and then we doubt (negative) and do hypothesis If the result would be P.05 then we would accept that our doubt was true and there is an alternate hypothesis Keep in mind that the only reason we are testing the null hypothesis is that we think it is wrong. Six Sigma Training at Gurgaon, Six Sigma Training at Noida, Six Sigma Black Belt training in Delhi or Six Sigma training in India, no matter which location you shall want to choose for your Six Sigma training program, Advance Innovation Group shall always be there to support you. The training methodology designed by Advance Innovation Group is to make your concepts clear moreover from a practical concept rather than keeping it theoretical. Null Hypothesis: Null hypothesis (Ho) is a stated assumption that there is no difference in the parameter for two or more population. (P value >.05) The null hypothesis, H0, represents a theory that has been put onward, either because it is judged to be true or because it is to be utilized as a basis for argument, but has not been proved. For example, in a clinical trial of a new drug, the null hypothesis might be that the new drug is no better, on average than the current drug. We would write H0: there is no sameness between the two drugs on the mean.

2. We give special consideration to the null hypothesis. This is due to the fact that the null hypothesis relates to the statement being tested, whereas the alternative hypothesis relates to the state to be accepted if / when the null is rejected. If we cogitate "Do not refuse H0", this does not needfully mean that the null hypothesis is true; it only advises that there is not adequate evidence against H0 in favor of H1. Rejecting the null hypothesis then suggests that the alternative hypothesis may be true. Alternative hypothesis: Alternative hypothesis: the observe diff or relationship b/w two population is real (P The alternative design of experiments course hypothesis, H1, is a statement of what a statistical hypothesis test is set up to establish. For example, in a clinical trial of a new drug, the alternative hypothesis might be that the new drug has a different effect, on average, compared to that of the current drug. We would write H1: the two drugs have different effects, on average. The alternative hypothesis might also be that the new drug is better, on average than the current drug. In this case, we would write H1: the new drug is better than the current drug, on average. The final conclusion once the test has been carried out is always given in terms of the null hypothesis. We either "Reject H0 in favour of H1" or "Do not reject H0". We never conclude "Reject H1", or even "Accept H1". If we conclude "Do not reject H0", this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against H0 in favor of H1. Rejecting the null hypothesis then suggests that the alternative hypothesis may be true. The probability value (p-value) P value represents the probability of concluding (incorrectly) that there is a difference in your samples when no true diff exists. Calculated by comparing the distribution of given sample data and an expected distribution and dependent upon test being performed.