For the control group the SS is: X X-M ( X - M ) 2 18 3 9 - 6 36 14 -1 9 20 5 25 15 0 0 10 - 5 25 21 36 15 0 0 12 -3 9 16 1 0 0 SS= 142 With the SS for both groups we can now get our pooled variance number: SS1 + SS2 52 + 142 194 Sp =12.13 of 1 + df 2 7+9 16 Now we can do the independent t test equation: M1 - M2 18-15 3 3 t = 1.22 - =2.45 12.13 V.81 +.67 15 18 n1 1 2 Okay we can see the result of our t test t=2.45 is larger than the critical t of t=1.746, the p value of t=2.45 is p<.05. bud up works as we can see from the means that there are more flowers in group while know is a significant effect do not how strong it is. again will an size for this hypothesis test. start with cohn d equation and continue to use pooled variance s p error value: m1 m2_ v12.13 using cohen table sizes large effect. macbook pro g search or type url o delete t y u r f h c k . v b n mpat-t page of> depe test e he inc In this section we will perform an independent t-test on two samples and then using this example discuss effect size for the data. ormu A researcher wants to find out if applying their fertilizer "Bud UP" makes more flowers on plants. They have two groups of plants. Their control group (only water) has 10 plants and their experimental group (using Bud UP) has 8 plants. The raw data for the number of flowers for both groups are as follows: Bud UP: 21, 14, 15, 18, 16, 21, 19, 20 M=18 water: 18, 9, 14, 20, 15, 10, 21, 15, 12, 12, 16 M=15 We are going to test this using the 4 step process of hypothesis testing: Step 1: create the two hypotheses Ho: The number of flowers produced is not different between the two groups. HA: The number of flowers is significantly more in the Bud UP group. Step 2: Set the criteria This is a one tailed test and we will use an alpha level of a=.05. We can obtain the df for our study using: df=(n1-1)+(n2-1)=7+9=16 Checking with th t-table this criteria gives us a critical t-value of t=1.746 Step 3: Calculate the independent t test We first have to get the sums of squares (SS) so we can do the pooled variance equation: For the experimental group we have: X X-M (X-M)