Purpose
Any hypothesis which differs from a given null hypothesis is called the alternative hypothesis. This is designated as Ha, or H1.
While the null hypothesis is a statement of “no effect” or “no difference”, the alternate hypothesis will state that a difference or effect exists.
Anatomy
Reference: Basic Statistics by M. J. Kiemele
Terminology
A. The Null Hypothesis.
B. Different Alternative Hypotheses, which if accepted state that observed differences between mo and m1 are statistically significant, and cannot be explained away as random variation in the samples.
C. Two Sided Alternative Hypothesis; it is accepted if: the new process mean m1 is significantly less or greater than the old process mean mo
D. One Sided Alternative (Directional) Hypothesis; it is accepted only if: the new process mean m1 is significantly less than the old process mean mo
E. One Sided Alternative (Directional) Hypothesis; it is accepted only if: the new process mean m1 is significantly greater than the old process mean mo.
Purpose
The null hypothesis, designated as H0, is the assertion being tested in an hypothesis test. Usually the null hypothesis is a statement of “no effect” or “no difference”.
For example the null hypothesis to validate a process change when the old mean and new mean are mo, and m1 respectively, will state that both process means are the same i.e. H0:mo= m1.
Anatomy
Reference: Basic Statistics by M. J. Kiemele, S. R. Schmidt and R. J. Berdine, The Vision of Six Sigma: Tools and Methods for Breakthrough by M. J. Harry
Terminology
A. Null hypothesis used to check whether new process mean m1 differs from old process mean mo. Test is to determine if change in mean is simply due to random variation, or whether the process has changed, and the new mean is significantly different from the old one. The null hypothesis is assumed true until sufficient evidence is presented against it.
B. Plots of different means, from data taken from the old process and the new process. The null hypothesis will state that all the data belongs to the same underlying population, with the new process essentially equivalent to the old one.
C. Null hypothesis used to check whether new process standard deviation differs from old process standard deviation.
D. Plots with different standard deviations, from data taken from the old process and the new process. The null hypothesis will state that all the data belongs to the same underlying population, with the change in standard deviation being due to random variation only, i.e. the process has in fact not really changed.
May 10, 16 09:24 PM
A Quality Control Plan is a documented description of the activities needed to control a process or product. The objective of a QCP is to minimize variation.
May 10, 16 08:49 PM
The Largest Collection of Free Six Sigma Tools and Training on the Web!
May 10, 16 07:28 PM
The Weibull distribution is applicable to make population predictions around a wide variety of patterns of variation.