Difference between T-Test, One Way ANOVA And Two Way ANOVA

T-test and ANOVA (Analysis of Variance) i.e. one way and two ways ANOVA, are the parametric measurable procedures utilized to test different hypothesis. As these depend on the assumptions like the population ought to be normally distributed, random sampling, homogeneity, independent, measurement scale, individuals frequently misinterpret these terms.

T-Test

A t-test usually called student’s t-test is test for statistical hypothesis in which the test measurement takes after a t-distribution according to null hypothesis. It can be utilized to figure out whether two groups of data are altogether not quite the same as each other.

One Way ANOVA

Infield of statistics, one-way ANOVA is a method utilized to compare the means for three or more than three samples by utilizing the F distribution. This method can be utilized just for numerical data.

Two Ways ANOVA

In field of statistics, the two-way ANOVA is an augmentation of the one way ANOVA that looks at the impact of independent variable (two) on one dependent variable. The two-way ANOVA additionally evaluates if there is any interface between them.

T-Test Vs One Way ANOVA Vs Two Way ANOVA

  • Definition:

T-test is actually the test of hypothesis that is utilized to compare means of two samples.

One way ANOVA is also test of hypothesis, utilized to test the correspondence of three or more populace means at the same time utilizing variance.

Two ways ANOVA is technique used in statistics, which is used to find the interface between factors and the affecting variable.

  • Variables:

In t-test, independent variable is two level definite variables like gender.

In one way ANOVA, one independent variable is there.

In two ways ANOVA, there are two independent variables.

  • Compares:

In t-test, means of two samples are going to be compared.

In one way ANOVA, there are three or more than three levels related to one factor are going to be compared.

In two ways ANOVA, there are the many levels related to two factors which are compared.

  • Observations:

There is no minimum or maximum size of observation for t-test but there must be observations that can compare and same in each group.

In one way ANOVA, the number of observations should not be same in each group.

In two ways ANOVA, the number of observations should be same in each group.

  • Design of experiment:

In t-test, there are two conditions of designs of experiment that are experimental and control.

In one way ANOVA, there are also two conditions of experimental design which should be satisfied that are randomization and replication.

In two ways ANOVA, there are three conditions of experimental design which should be satisfied that are local control, randomization and replication.

Conclusion

From above article we can conclude that t-test is utilized to compare means of two samples, one way ANOVA is utilized to test the correspondence of three or more populace means at the same time utilizing variance while two way ANOVA is used to find the interface between factors and the affecting variable. In t-test and one way ANOVA, there are two conditions of designs of experiment but different from one another while in two way ANOVA, there are three conditions of experimental design.