Are These T-Test and Effect Size Calculations Correct?

  • Thread starter nobahar
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    T-test
In summary, the researcher found that the results from the two conditions were different. They used a t-test to test if there was a difference between the two means, and found that there was a significant difference. They also found that the s.d.s. was different, which is significant.
  • #1
nobahar
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Hello! I hope it's okay to ask stats questions...
I think the calculations are correct, but I would appreciate it if someone could check for me!
These are the results from the exp. using repeated measures. My hypothesis is that the reuslts in condition 2 should be less than condition 1.
Cond 1
49
41
42
44
44
42
66
57
44
66
66
59
Cond 2
43
39
36
42
48
41
38
44
34
40
32
43

I know working out the standard deviation is tedious, but I checked with excel and it matched (I used n-1, so I got the population, not just the sample).
So, for condition 1:
mean=51.66666667
s.d.= 10.36895132
standard error= 2.99325842
and condition 2:
mean= 40
s.d.= 4.51260859854
standard error= 1.30267789
These (I think) are correct, its the t-test, p value and effect size I would greatly appreciate some feedback for.
For the t-test I used the realted t-test, since its repeated measures (same participant in both):
ttestaudio.jpg

I got t= 3.346118674
are the units sd?
and DoF would be 11, and since I specified a direction, I got p=0.003261139 (one-tailed); which is significant? since P is less than 0.05?
and for effect size:
effectsizeaudio.jpg

I got (d)= 1.567935986
Can somone verify the t-test, effect size and p value for me? Please! I used fractions at some points where I could quickly get the data (I wrote it down), in ideal circumstances should I use fractions for all the 'inputs' (e.g. stand. dev of difference, etc.), if possible, since its more 'accurate'?
Thanks! I now it's a lot to ask, but I would geratly appreciate it! I realize the formuale are slightly different depending on the narue of the experiment, I think I chose the right ones!
 
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  • #2
It's been a while since I've done anything with statistics, but I think you might not be using the right form for t. The form I've included is for a two-sample t test, which seems to me the right one for your problem.

Instead of the standard error, I think you might need to use a pooled standard deviation.

[tex]t = \frac{X - Y}{S_p\sqrt{\frac{1}{12} + \frac{1}{12}}}[/tex]
where X and Y are the sample means for the two conditions, and [tex]S_p[/tex] is the pooled s.d., which is given as
[tex]\sqrt{\frac{(n - 1)s_x^2 + (m - 1)s_y^2}{n + m - 2}}[/tex]
For your problem, n = m = 12.

Your use of fractions and decimal values probably won't affect your values, since you apparently used quite a few decimal places in your calculations.

The t distribution is close to a normal distribution with mean 0 and s.d. 1. For n around 30 there's no difference between the two distributions. That should answer your question about units of s.d.s.

Hope this is helpful.
 
  • #3
Thanks for the response!
I've got to do some reading... I haven't come across (or I don't think I have) the pooled s.d. yet.
I kow the related and independent t-tests. The latter being the (mean 1 - mean 2)/sqrt{(variance 1/n1)+(variance 2/n2)}.
 
Last edited:

What is a T-test?

A T-test is a statistical method used to compare the means of two groups. It determines whether the difference between the means of the two groups is statistically significant, meaning it is unlikely to have occurred by chance.

What is the purpose of a T-test?

The purpose of a T-test is to determine if there is a significant difference between the means of two groups. It is commonly used in research studies to compare the effectiveness of two treatments, or to see if there is a difference between groups of participants.

How is a T-test different from an ANOVA?

A T-test is used to compare the means of two groups, while ANOVA (Analysis of Variance) is used to compare the means of three or more groups. T-tests are more appropriate when there are only two groups being compared, while ANOVA is better suited for comparing multiple groups at once.

What is effect size?

Effect size is a measure of the magnitude or strength of the relationship between two variables. It indicates the size of the difference between two groups or the strength of the association between two variables. Effect size is important because it provides a standardized measure of the magnitude of an effect, making it easier to compare results across studies.

How is effect size calculated?

Effect size can be calculated in different ways depending on the type of data and the statistical test being used. For a T-test, the most commonly used effect size measure is Cohen's d, which is calculated by taking the difference between the means of the two groups and dividing it by the standard deviation. Other measures of effect size include Pearson's correlation coefficient and odds ratio.

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