Please note Edit Below:
I have to establish the statistical relevance of a hypothesis which is of the form
H0 = The event lead to an increase in performance
Ha = The event caused a negative or no effect in performance
The data set that i have has achievements before and after the event.
The question is how do I run the hypothesis testing?
More precisely-> for a given time i have a number of records measuring performance both before and after the event like below
I was wondering what parameter to take as mu in the 2-tailed T test. Should i create another metric combining both the achievements which will be a measure of the performance? If yes, any suggestion on how i can make such a metric?
Any suggestions beyond the above will also be helpful.
I figured that the 2 sample T test would fit the case where my hypothesis would be of the form
Ho : mu1 - mu2 = hypothesised mean difference (in this case 0)
Ha : mu1 - mu2 < / > / not equal the hypothesised mean difference
Assuming independent populations and normally distributed data
But my data is like the following as mentioned above.
Is it right to compare the means of the achievements through the above test as the data is not at the same level .. as in it is at a person product level.
Will ANOVA makes more sense for the above dataset to see the difference between the performances before and after the event?
Is it possible to do the analysis at this level or should i club the products and have the data like the following and then go ahead with the 2 sample T test?