When we start our study on Analyze and we are taught that the whole study of Hypothesis and Probability is based upon only one Mathematical number output through any test you do that is “P” value, it seems very strange at the first instance. Even when we review English alphabet “P” is like 16 Alphabet in the English vocabulary, however, basis the study completed, I can conclude that “P” is the One and Only value in Sigma study which shows the statistician or any individual running Sigma Statistical tests the relation of one data value with another, the data normality, the variation in data, data randomness can prove your assumption to be true or false basis the value or output been gained against “P” through any test been performed.
Now, when we talk about an ideal world, we say that 100% of the population has the same or different behavior but this is only the assumption till the time proved statistically which is globally acceptable. In the real terms, the population behavior is always captured or proved through the changes, variation or random movements seen in the significant Sample been taken against the whole population. Now, when I gauge the Sample behavior against the Population, I always place it on a scale where I have 95% (0.95) Confidence Interval, and 5% (0.5) error value, where statistically I can say that 95% of my Sample behavior is accurate, and I can undertake 5% (0.5) error into consideration, now, post running the tests like Run Charts, Normality and Graphical summary test through Minitab, I can derive the P value which can either prove that my assumption about the Population based upon the sample been undertaken is Correct or Incorrect.
Now, these correct and incorrect assumptions is divided into two major statistical terms – Null Hypothesis and Alternate Hypothesis, where Null means that Everything in this world is perfect there is no relation, or variation amongst the data values, whereas Alternate Hypothesis is opposite which states that there is relation between two data values, and based upon trend it proves the variation and states that “Data is Not Normal”. Now, data normality test depends upon our Negative assumption which universally we accept, that any assumption which we would have, we can state that assumption would be negative, to start with “my assumption is that Data is Not Normal” and statistically if my P value derived is Equal (=) or Greater Than (>) 0.05, I would accept Null Hypothesis which states that Data is Normal and No variation, or relation exists between the data values, on the other hand when, if I my P value is Less Than (<) 0.05, I would accept my Alternate Hypothesis which states that “Data is Not Normal” and variation and relation between the Data values exists.
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The whole hypothesis study depends upon the P values outcome against the 0.05 figure in terms of =, >, < and depicts the Null or Alternate hypothesis.
