 We are talking about significance of the results and we are concentrating on the p-value and test statistic value to you know conclude and make a decision about an alternative hypothesis. But one thing that I have already told you is that we sometimes have a direct effect on the size of the sample. Because sometimes the difference is very tiny but because of the large sample even more tiny differences be detected that test. But if the sample size is very small, as we just saw that there were clearly swimmers or soccer players scores different or swimmers definitely score on the test was higher as compared to the soccer players but because of the small sample size we could not approach the significance level. So p-value is not so meaningful what we need to report actually is the effect size. Effect size, what is the effect size? Effect size tells us that what is the magnitude or strength of the difference. If you are comparing two groups soccer and swimmer players then you are saying that there is no significant difference. So what was actually the magnitude or the strength of the difference between the groups? So people can understand is it due to sample size or is it really a huge difference exists between the groups. So there are number of different effect sizes in statistics. The most commonly used to compare the groups are partial eta square which we do in ANOVA and that in SPSS when we run ANOVA and we check the option that it gives us the effect size automatically in your output table it gives us the value of partial eta square. But for T test we use for independent samples that is Cohen's D. Cohen's D means that it tells us how much is the strength and magnitude of the difference in both groups. But Cohen's D is not directly calculated in SPSS. For that we have the calculator. If you go to net, if you see Cohen's D calculator it will give you online. We have to put some information in it and then put that information and it will tell us the effect size. I will show you its image now. For the independent sample T test Cohen's D is determined by calculating the mean difference between two groups and then dividing the mean by the pooled standard deviation. So we first look at the difference of both the mean and then divide it from the pool standard deviation. I wanted to say that when you go online and you type in a Google browser Cohen's D calculator it gives you table like this and in that you have mean of group one standard deviation of group one and sample size of group one and in this way you have to give it group two. So when you say and hit the button to calculate it, it gives you the value of Cohen's D here. Now for Cohen's D a standard or criteria is already given that if Cohen's D value is between 0.2 or 0.2 to 0.5 then it is a small effect size. Small effect size. If our value is between 0.5 to 0.8 then we say that it is medium and if our value is greater than 0 then we say that it is a large effect size. So this is terminology that we use to report or to tell about the effect size. So if the value is around 0.2 or lower than 0.5 it is a small if it is 0.5 and greater but lower than 0.8 it is a medium effect size and if it is greater than 0.8 it is a large effect size. What value have we got? In this we have minus mean 16 minus 8.8 which we have divided on the standard error. So our effect size 1.1 came which is a large effect size. So you see the results are significant. So if you are reporting as a student and you see that our sample size was 13 and you have reported that it is not significant. So the effect size is telling you a different story. It is telling that there is a large effect size. So the magnitude difference is bigger, it is large, it is greater. So how we will report all these results? Final step, you have run t, you have calculated the value of p. So this is our standard way of American Psychological Association, APA format. You will report the results like this. Definitely you will add a description like we have already said, one or two lines, description and then you will plugin the values. An independent sample t test was conducted to compare the neurological test scores for swimmers and soccer players. There were no significant differences in scores for swimmers and then we will report its mean or standard deviation, m or st italicized yoga and soccer players and then we will report its mean or standard deviation. With the t value of its parenthesis, we will give degrees of freedom, which is 11, t value is 2.02 and p is equal to 0.069. Remember that I said that when our p value is not significant, then we will exactly report that value. But if we first report that p value is 0.04, then we can say that p is smaller than 0.05. Then we can report like this, but we hear a state the exact p value, that is 0.069 or two-tailed biaga mention karenge. The magnitude of the differences in the means, mean difference is 2.86 with a 95% confidence interval of this was scores days 1.105. So far as per APA requirement, this is how you will report the results of t, p, effect size and confidence intervals, mean and standard deviation. Usually we do not give tables for t, but we give all the information in the text with the one or two statements, we give all this information.