When conducting a statistical test, usually 
we look at our T-value, P-value or F-value. 
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  1) T-value
   If we choose a 5% level, the critical value of a T-value is 2 or -2. 
       It means that if our T-value is bigger than 2 or less than -2, we reject the null hypothesis. 

  2) P-value
       If we choose a 5% level, the critical value of a p-value is 5%. 
       It means that if our p-value is less than 5%, we reject the null hypothesis. 

  3) F-value
       If our F-value is bigger than our critical F-value, we reject the null hypothesis. 
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Projects taken already
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  #  Name of the topic            Group                             Date to present 
 --  ----------------------       ---------------------------       ----------------
 1   Test of the January Effect     Amarendra
 2   52-week high trading strategy  Steve,Ed,Guodong, Rick
 3   Financial statement analysis   Hariston, Eric
 4   R package quantmod             Bennett and Sharan
 5   PerformanceAnalytics           Hongjian, Chris, ZiAng

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Videos
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StatsCast: What is a t-test?  (9m56s)
     https://www.youtube.com/watch?v=0Pd3dc1GcHc

Student's t-test (10m10s)
      https://www.youtube.com/watch?v=pTmLQvMM-1M

t-test in Microsoft Excel
     https://www.youtube.com/watch?v=BlS11D2VL_U
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Links
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  Statistical Significance (T-Test)
      http://docs.statwing.com/examples-and-definitions/t-test/statistical-significance/

  T-test
      http://www.investopedia.com/terms/t/t-test.asp
  
  Student's t-test
     https://en.wikipedia.org/wiki/Student%27s_t-test

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Sample vs. Population 
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   When simulating variance, we have the following two formulae, one for the sample 
         while the other is for the population. 
    Assume that we have n observations, x1, x2, ..., xn

                           x1 + x2 + x3 + ..... + xn
            mean   = -------------------------------------
                                    n
    Variance based on population: 
               (x1-mean)^2 + (x2-mean)^2 + ...  + (xn-mean)^2
      var1=  -------------------------------------------------
                                 n
    Variance based on sample: 
               (x1-mean)^2 + (x2-mean)^2 + ...  + (xn-mean)^2
      var2=  -------------------------------------------------
                            n -1

  This is true for standard deviation:
     std1= sqrt(var1)    # standard deviation based on population 
     std2= sqrt(var2)    # standard deviation based on sample 

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