# 11- Student's t.test in R programming

The t-test uses to examine the difference of means of two groups of data.

Default in t-test assumes unequal variance and applies the Welsh df modification

Create data:

```
group_a<- c(86, 97, 99, 100, 101, 103, 106, 110, 112, 113)
group_b<- c(50, 100, 98, 95, 90, 105, 110, 95, 100, 112)
```

#### One sample t-test

```
t.test(group_a)
```

#### Independent (unpaired) t-test

Used for an independent data set that is distributed identically. For example, randomly divided 200 samples of treatment cases to 100 as treatment group and 100 as a control group.

```
tt<-t.test(group_a, group_b)
tt
t.test(x=c(2, 4, 6), y = c(2, 4, 6))
t.test(x=c(-2, -4, -6), y = c(2,4,6))
t.test(x=c(2, 4, 6), y = c(4, 8, 12))
```

#### Paired t-test

Used for equal pairs of similar units, or one group of units that has been measured twice, for example, blood pressure before and after treatment.

```
df <- data.frame(Name = c("Jon", "Ali", "Aviar", "Didar", "Shilan", "Tom"),
first_exam = c(70, 41, 85, 58, 46, 90),
second_exam = c(45, 80, 55, 95, 85, 80)
)
t.test(df$first_exam, df$second_exam, paired=TRUE)
```

#### Extract p-value and the value of the t-statistic

```
names(tt)
t.test(group_a, group_b)$p.value
tt$p.value
tt$statistic
# or
tt[['statistic']]
```

#### Interpret the result

In the output:

- t is the t-test statistic value,
- df is the degrees of freedom
- p-value is the significance level of the t-test
- confidence interval is the confidence interval of the mean at 95%
- sample estimates is the mean value of the sample

If p-value >0.05, indicates that means of groups of data are not significantly different.

If you like the content, please SUBSCRIBE to my channel for the future content

To get full video tutorial and certificate, please, enroll in the course through this link: udemy.com/course/r-for-research/?referralCo..

### Did you find this article valuable?

Support **Azad Rasul** by becoming a sponsor. Any amount is appreciated!