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R Rajesh has provided complete and correct answer for the question. Other answers were disqualified due to plagiarism. Congratulations Rajesh!

Definition of P-value It is the probability of getting the observed values if the null hypothesis is actually true. It can also be written in this way. Let us see an example . Suppose there is a magician who has a coin in her hand.Then let us say that Null hypothesis (Ho) : a proper coin (which can show 'Heads' and 'Tails' regularly, when flipped) Alternate Hypothesis (Ha): a magical coin which can show only 'Heads'. Now let us see the observed values each time when the coin is flipped. Let us denote Observed Value as OV , Probability as P. When the coin is tossed for multiple times, assume that below are the cumulative probability results : a).OV1 = H; P1=1/2=0.50 b).OV2=H;P2=0.25 c).OV3=H;P3 = 0.12. d).OV4=H;P4=0.06 e).OV5=H;P5=0.03 ..... As we traverse through this further, we find that this coin is not normal!!. We come up with a conclusion (with the above information as strong evidence) that this is a magical coin and therefore reject the null hypothesis. Common Misunderstandings with respect to 'P' values. 1. 'P' value compared with 0.05. Often P value is compared with a value of 0.05, which is a pre-defined value given for alpha- which determines the statistical significant value for the given hypothesis.Always we put the equation if P<= 0.05, then we say the alternate hypothesis (Ha) is true. or P > 0.05 indicates that Null hypothesis is true(fail to reject null hypothesis). The point is 0.05 is not a pre-defined value for all cases. It can vary and it depends on how much significance level you want for that study (hypothesis case) to be. Therefore to say, P > 0.05 will ensure the null hypothesis to be true is incorrect. It should be ideally said as P > the chosen 'alpha' value should result in the 'null hypothesis' being deemed true. Eg: Let us see with an example as how this alpha value can vary . Null hypothesis(Ho): The person is innocent Alternate Hypothesis(Ha): The person is guilt. Imagine which would be the biggest crime ? Freeing a guilty person or jailing an innocent. You want to ensure to the maximum hilt, that an innocent person should not be jailed and that percentage of making that mistake should be extremely low(which is what type 1 error is all about- rejecting null hypothesis when it is actually true). Therefore, in this study, we want to lower the alpha value so that there is a negligible chance of an innocent person getting jailed. So in this case, ideally you want your alpha value to be something like 0.01 or less than that . This is the reason why the jury (in a civil court) asks for strong evidences and the fact that it should convince the judges(because they got strong evidences(or circumstantial evidences) to provide the right verdict. Note: In Indian law, it is slightly different. A person can be charged on suspicion which is altogether a different issue and the person or his/her has to defend his/her innocence. But most of the countries follow a rule where your are innocent until proven guilty. The takeaway point here is that the p-value should not be statically compared with 0.05 (which is a pre-defined level of significance value used in general) . The significance value (alpha) can vary. It depends on the consequences that type1 error and type2 error can produce. In general , if there is a greater impact/consequence on getting type1 error, then alpha value should be minimal (lower than may be 0.05 , in many such cases). 2.Showing implications of the 'P' value It can indicate that there is a difference (when P is low and is < alpha) but it does not portray the practical implications or impact that it has. 3. Low P-value and rejecting of Null hypothesis. When P-value is low and is less than the alpha value, say P < 0.05(assuming this alpha value), then it means a statistical evidence to reject the null hypothesis is shown. It however does not mean that this will prove the alternate hypothesis . Conclusion: It is easy to get confused or misunderstood that p-value is all about stating either null hypothesis is right or wrong. It helps us in understanding the statistical inference that we get from a study. Also the alpha value and also sample size needs to be decided before the data collection is done. Alpha value is to be decided on the nature of the study and it depends on the consequences that the type 1 and type 2 errors can make.

Dear Ransingh A very good question and the link shared by VK will help you visualize how CLT works. I want to highlight a common misconception about Central Limit Theorem. It is probably one of the most misunderstood concepts in Lean Six Sigma. Most of the people assume that if they have a large sample size (read greater than 30), then the data set follows normal distribution. This is far from truth. Irrespective of the sample size, the sample will always follow the distribution of the original data set. So if the original data set is Not Normal, then the sample (be it size 1 or 2 or 10 or 30 or 100 or however big) will also be Not Normal. Then where does CLT apply? CLT applies on the distribution of the sample means or sample sums i.e. if i pick up multiple samples from the Not Normal data set, calculate either the sum or the mean of all the samples and plot them on a histogram, then it will follow a Normal distribution. For e.g. consider a roll of a single dice. Possible values are 1,2,3,4,5,6 each having the same probability. A common misconception would be if i roll the dice multiple times (say 6000) times, I will get a normal distribution. This is not true. Roll of a dice follows a Uniform distribution and hence if you roll it 6000 times, it is likely that 1 through 6 will occur 1000 times each. However, what happens if 2 dice are rolled and sum of each roll is noted. The possible values are 2,3,4,5,6,7,8,9,10,11 and 12. Here however the probability is not the same. Prob. of getting 2 = 1/36 (only 1 combination will give 2) Prob. of getting 3 = 2/36 (2 combinations will give us 3) Prob. of getting 4 = 3/36 (3 combinations will give us 4) and so on...... 7 has the maximum probability (6/36) of occurrence while 2 and 12 have the least (1/36). Now, if I roll the 2 dice for 6000 times and plot the sums of each roll on a histogram, the plot will start resembling a normal distribution because of the variation in the probabilities of each number. Here if you notice closely, 1. The original distribution is Not Normal 2. Taking 2 data points from the original data set will give me a sample (equivalent to rolling of 2 dice). Then for each sample, the sum is being calculated and plotted 3. CLT is being applied on the sum and not on the individual data points The same is evident in the animation link shared by VK. So let's be aware of the misuse of this theorem and apply it correctly. P.S. there are multiple online sources where you can also find the mathematical proof of the the Central Limit Theorem.