R-programmingHow-ToBeginner · 4 min read

How to Perform t-test in R: Syntax and Examples

In R, you perform a t-test using the t.test() function. It compares the means of two groups to see if they are significantly different. You provide the data vectors or a formula and specify options like paired or unpaired tests.

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Syntax

The basic syntax of the t.test() function is:

t.test(x, y = NULL, alternative = "two.sided", paired = FALSE, var.equal = FALSE, conf.level = 0.95)

Where:

x: numeric vector of data values or a formula.
y: optional numeric vector for two-sample tests.
alternative: type of test - "two.sided", "less", or "greater".
paired: TRUE for paired t-test, FALSE for independent samples.
var.equal: TRUE assumes equal variances, FALSE uses Welch's test.
conf.level: confidence level for the interval.

t.test(x, y = NULL, alternative = "two.sided", paired = FALSE, var.equal = FALSE, conf.level = 0.95)

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Example

This example shows how to perform an independent two-sample t-test comparing two groups of data.

group1 <- c(5.1, 4.9, 5.0, 5.2, 5.3)
group2 <- c(5.8, 6.0, 5.9, 6.1, 6.2)
result <- t.test(group1, group2)
print(result)

Output

Welch Two Sample t-test data: group1 and group2 t = -7.0711, df = 7.998, p-value = 0.000105 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1.370645 -0.829355 sample estimates: mean of x mean of y 5.1 6.0

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Common Pitfalls

Common mistakes when using t.test() include:

Not specifying paired = TRUE for paired data, which leads to wrong results.
Assuming equal variances without setting var.equal = TRUE when appropriate.
Passing non-numeric data or vectors of different lengths.

Always check your data and test assumptions before running the t-test.

## Wrong: paired data but paired=FALSE (default)
group1 <- c(100, 102, 98, 101, 99)
group2 <- c(102, 103, 97, 100, 100)
t.test(group1, group2)

## Right: paired=TRUE for paired samples
t.test(group1, group2, paired = TRUE)

Output

Welch Two Sample t-test data: group1 and group2 t = -0.36515, df = 7.998, p-value = 0.724 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -3.927 2.527 sample estimates: mean of x mean of y 100.0 100.4 Paired t-test data: group1 and group2 t = -1.8974, df = 4, p-value = 0.134 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -3.370 0.570 sample estimates: mean of the differences -1.4

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Quick Reference

Parameter	Description	Default
x	Numeric vector of data or formula	Required
y	Second numeric vector for two-sample test	NULL
alternative	"two.sided", "less", or "greater"	"two.sided"
paired	Use paired t-test if TRUE	FALSE
var.equal	Assume equal variances if TRUE	FALSE
conf.level	Confidence level for interval	0.95

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Key Takeaways

Use t.test() in R to compare means between two groups.

Set paired=TRUE for paired samples to get correct results.

Use var.equal=TRUE only if you know variances are equal.

Check data types and lengths before running the test.

Interpret p-value to decide if means differ significantly.