T - Test Meaning

T-test is utilized in the form of a hypothesis testing mechanism or tool –allowing testing of some prediction that applies to a population. It can be defined as a statistical test utilized for comparing the means of two distinct groups. T-test serves to be a parametric test of the overall difference - implying that it is going to make similar assumptions about the data in comparison to other parametric tests.

A T-test appears like a T-statistic. It also represents the t-distribution values along with the overall degrees of freedom for determining the overall statistical significance. For conducting a proper test with 3 or more means, it is important to make use of analysis of variance.

T - Test Example

Primarily, a T-test allows comparison of the average values of two data sets. It determines if they are derived from the same population. For instance, if you take up the sample of students belonging to Class A & another sample featuring students picked from Class B, then one cannot expect all of them to feature the same Standard Deviation & mean. At the same time, samples taken from the respective control group that has been assumed, and the ones obtained from the group that has been drug-prescribed, it would reveal a slightly different standard deviation and mean.

T - Test Formula

T = (m -µ) / (s/ square root of n)

Where,

• t = student’s test,
• m = mean,
• µ = theoretical value,
• s = standard deviation, and
• n = variable set size.

On mathematical grounds, the T-test derives the sample from the respective two sets while establishing the problem statement. It is achieved by assuming some null hypothesis that the available two means tend to be equal. On the Basis of the applicable formulae and types of T-test, specific values are calculated while being compared against the available standard values. The assumed hypothesis may be rejected or accepted completely.

The T-test serves to be one of the many tests available out there for the given purpose. Statisticians are expected to include more tests in comparison to the T-test example for examining more values and testing with large-sized samples.

For a sample size that is quite large enough, statisticians utilize the concept of the Z-test. Some of the additional testing mechanisms are the f-test and the chi-square test.