Blackboard Research Writing

Blackboard Research Writing

R  Two-Sample Differences (t-test)

Get knowledge on R two sample differences. The Student’s t-test is a popular method for comparing the mean values of two normally distributed samples. These samples are referred to as parametric because we make several assumptions about the population from which they are drawn. Thus, the t-test represents a parametric statistical test. The t.test() command in R will run many variants of the t-test. It is derived from the formulae;

t.test(x, y, alternative, mu, paired, var.equal)

whereby; x= a numerical sample

                y= a second numerical sample

               alternative= the default setting for comparing means is “two.sided”. You can also enter “less” or “greater” as a value.     

               mu= The actual mean value (or mean difference). The default value is 0.

               Paired= paired = FALSE, is the default value. This presupposes that the samples are independent. In matched pair test, the option paired = true, is utilized.

                equal= Var.equal = FALSE, is the default. The variance of the two samples is treated independently in this case. When you put var.equal = TRUE, you run a classic t-test with pooled variance.

In most circumstances, two independent samples are compared in R. As shown in the example below;

two independent samples are compared in R

You can perform a single-sample t-test in R by explicitly stating the mean that the single variable should be compared with:

single-sample t-test in R

When you have matched pair data, you can set the parameter, paired = TRUE.

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