A T-Test is a hypothesis testing tool used to test an assumption of a given population. It is a type of inferential statistics used to determine the significant difference between the means of two groups with similar features.
The samples are compared based on their means and are very easy to compare samples of independent groups.
Let’s take a look at the pros and cons of using T-test to compare samples.
1. Essential for generalization: The results achieved after t-test are useful for concluding if they are actually correct, they can be applied to the entire population.
2. Easy to interpret: It is very easy to interpret the output of independent samples. The output tells you how different the mean of one sample is from the mean of another group. It also indicates the mean of each group and the average difference between the groups. A larger t-score indicate the groups are different and a smaller t-score indicate the groups are similar
3. Robustness: It assumes that the independent samples of two populations are normally distributed and have the same variance. The t-test is fairly robust to the violation on the first assumption since there are two samples from a population which have unequal variances
4. Easy to calculate: It is easy to calculate data from two samples with the aid of a computer. Standard software programs that support statistical functions like Microsoft Excel can be used to calculate t-test data.
5. Easy to gather data: A small number of subjects for independent t-test samples are required. Only one value from each subject is needed;
it requires values of subjects from the two sample groups on a quantitative variable.
6. Determine source data: T-test enables us to compare the average values of two data set samples and determine whether the sample subjects come from the same population.
7. Easy to understand:
The t-test formula for independent samples is easy to understand. This makes it easy to know what is going on without needing much statistical training.
8. Saves time: since the small sample size is needed for calculation, it not only saves on money but saves on the time required to collect and analyze large amounts of data.
9. Control of individual differences: T-test repeated measure design results in small effects since the amount of error from samples is very small. It also leads to good control of the individual differences. Only one group is available for testing and this can result in less noise of data.
10. Provide necessary information: The test gives you all the information you need to know about the population.
1. Difficult to find subjects: Getting the subjects for the sample data is very difficult and also a very expensive part of the research process.
2. Carry-over effects: When relying on paired sample t-tests, there are problems associated with repeated measures instead of differences between group designs and this leads to carry-over effects.
3. Small amount of noise: Although you might not worry about individual differences between the group data sets, there is still an individual difference between the groups and not every sample will react the same way, thus creating a small amount of noise.
4. Environmental impact: Independent t-test can help you determine the difference between sample groups but cannot help in controlling the effects of the environment. Environment changes may affect the output of t-test.
5. Multiple comparisons: T-test cannot be used for multiple comparisons because it results in type I errors. When conducting a paired t-test among a group of samples, it will be difficult to reject the null hypothesis.
6. A loss in degrees of freedom: When the df of a group test becomes lower, you need a higher t-value in order to reach the t-test significance and this creates a greater tradeoff between the greater power leading to fewer degrees of freedom.
7. Reliability of data: If the data collected violates the assumption of the t-test, then the output is unreliable.