T test on achievement data before and after an event 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. 

Edit 1:
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?  

 A: There are few misconceptions in your question. First, null hypothesis generally refers to "no effect" hypothesis that is verified by your research. Looking at your problem definition, it seems that you are interested in one-tailed hypothesis test about $\mu_\text{after} - \mu_\text{before} > 0$ (i.e. you are interested if your treatment improves achievements; see also this thread). Another misconception is about the data you have and the test you need. You say that you have

a number of records measuring performance both before and after the
  event

but in fact you have records measuring performance both before and after the event for different products, so you have two independent variables and you probably need rather ANOVA than $t$-test.
Referring to your edit, if you ignore in your analysis the fact that you had different products, that you can use $t$-test for such data. However in many cases this may not be the best way to go. As an example, you can search for Simpsons paradox, i.e. situation where your data shows something different if you look at it at local level, than on global level (see also this example). So such analysis may in some cases lead to different results than when you included information about products. This all depends on what is your data and what you want to learn from it (maybe products are something you can ignore, maybe not -- this is something you should consider). 
