Hi !!
The explainations given by all of you are great. Its
just a matter of how one understands it. Let me try
to make it little simple (for a practitioner):
0- Null hypothesis is status quo, that is, true till proven wrong
1- Whatever you’re trying to prove is HA (Opposite of Ho), i.e., prove if there is a difference between means, medians, variance etc.
2- You are not making conclusions about the samples but about the populations. In simple words you are trying to look at the kids (samples) and find out if they belong to same parent (population) or not. Therefore we always compare samples not the statistic (mean, median, std dev).
3- (1-P) is the confidence in accepting HA (in statistical world you never accept anything in hypothesis testing, you always reject or fail to reject the null hypothesis but for practioners above statement is simpler.)
4- in most commercial processes 95% or more confidence is good enough to accept the HA
5- So when you are testing means of two samples then alternate hypothesis is mean(A) is not equal to mean(B). If you get a p-value of 0.02 then you are (1-0.02 = 0.98) 98% confident that mean(A) is not equal to mean(B) so go for it.Let me know you need more info.Regards,
AG
