Independent t-Test: Unequal Sample Sets, 30 Samples Each

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In summary, the conversation discusses the need to perform an independent samples t-test with unequal variables on two sample sets, each with 30 samples. The t-test results in a t statistic of 7.81 and a p value of less than 0.001. The degrees of freedom is 43. The goal is to show that the difference between the averages of the two groups is statistically significant. There is some uncertainty about the correctness of the t-values and the need for a confidence interval.
  • #1
graham_ashe
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I need to perform an independent samples t-test with unequal variables on these two sample sets. There are 30 samples in each. So far, I've come up with the following information.

t(43) = 7.81, p < 0.001

degrees of freedom = 43
t statistic = 7.81
p statistic = <0.001

I need confirmation if that's correct. Also, what does this say about the statistics? Is it valid enough? I'm trying to show that the difference between the averages of the two groups is statistically significant. Thanks if you can help.


Set 01

1.073
0.598
0.876
1.434
0.636
0.705
0.524
0.561
0.401
0.516
0.916
1.243
0.628
0.615
0.537
1.166
0.635
0.615
0.909
0.728
1.180
1.223
0.582
0.833
0.964
1.038
0.636
1.077
0.427
0.602


Set 02

0.244
0.283
0.516
0.237
0.427
0.413
0.457
0.293
0.365
0.152
0.500
0.232
0.343
0.335
0.476
0.329
0.523
0.192
0.478
0.523
0.293
0.198
0.304
0.679
0.241
0.314
0.246
0.109
0.276
0.617
 
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  • #2
with a p-value that low, its obvius that the difference is statistically significant.

as for if you have all the t-values, I am not sure if its correct, do you have a confidence interval?
 
  • #3
The Ippster said:
with a p-value that low, its obvius that the difference is statistically significant.

as for if you have all the t-values, I am not sure if its correct, do you have a confidence interval?

Is a confidence interval necessary if I have a p-value instead?
 

1. What is an Independent t-Test?

An Independent t-Test is a statistical test used to determine whether there is a significant difference between the means of two independent groups. It is typically used when the two groups have unequal sample sizes.

2. When should an Independent t-Test be used?

An Independent t-Test should be used when you want to compare the means of two independent groups and the data is normally distributed. It is also appropriate when the sample sizes of the two groups are unequal.

3. How is an Independent t-Test performed?

To perform an Independent t-Test, you first need to calculate the mean and standard deviation for each group. Then, you calculate the t-statistic by dividing the difference between the two means by the standard error of the difference. Finally, you compare the calculated t-statistic to a critical value from a t-table to determine if the difference between the two means is significant.

4. What does it mean if an Independent t-Test is significant?

If an Independent t-Test is significant, it means that there is a significant difference between the means of the two groups being compared. This indicates that the two groups are statistically different and the difference is unlikely to have occurred by chance.

5. What are some potential limitations of an Independent t-Test?

Some potential limitations of an Independent t-Test include the assumption of normality, unequal sample sizes, and the need for independence between the two groups being compared. Additionally, an Independent t-Test does not account for other factors that may influence the results, and it can only be used to compare two groups at a time.

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