T-test are used when you have to test the hypotheses of an unknown population mean, it is used as a substitute.
a t distribution is computed through using sample variance . this is due to t- test using variance instead of error and the fact that t is used to test the difference between two means .t distribution are often bell-shaped and have a definite mean of zero . the shape if the t distribution changes due tho the degrees of freedom: the larger the degrees f freedom the closer to z the t distribution looks even though they are known to have more variability, be flatter and more spread out than z – distributions which hs a more central peak. the flatness of the t is caused by the variability of the scores in the distribution.
although t-test are exactly as great as they sound – they have their complications to for example testing variable and finding causation.so how useful are they, a study that helps ys e answer this very question as it shows that t-test help us to see whether the null hypothesis is true rest and thus has any given effect, it also dictates the variability af a sample and the effect of sample size has on this. this study plainly highlights the strength of such test in cases where there are a small amount if variables pitted against each othe as : t-test become more complex as more variables are added to the mix.