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Perform T-Tests in R I Types and Assumptions

Learn how to use the t.test function in R programming to perform one and two-sample t-tests, comparing means with equal and unequal variances.

As a data analyst with a Ph.D. in data science and five years of freelance experience, I often think about the intricacies of statistical tests. One such test that has always intrigued me is the T-test.

Have you ever wondered how researchers determine whether there is any statistically significant difference between two groups?

Or how they make confident decisions based on data? 

The answer lies in the T-Test, a statistical hypothesis test that allows us to compare means and assess whether the studied groups are distinct.

Perform T-Tests in R I 3-Types, Assumptions, and its Applications

Table of Contents

Key Points

  • The t-test is a widely used statistical method for assessing whether there is a significant difference in means between two groups or samples.
  • It is a parametric test that considers the variability within each group when comparing means.
  • Three main types of t-tests are Independent (also known as two-sample), Paired (also known as Dependent), and One-Sample T-test; the two-sample t-test is often utilized when you want to test unequal or var.equal conditions.
  • Key assumptions include random sampling, independence of observations, normal distribution, and homogeneity of variances.
  • Larger sample sizes enhance accuracy and the ability to detect significant differences.

What is a T-test?

The t-test is a statistical test used to determine if there is any statistically significant difference present in the means of two groups. It is a parametric test that compares means while considering variability within each group; read more. 

It helps researchers analyze the means of the different groups considerably from one another while considering variability within each group. It is extensively used in scientific research, social sciences, and business analytics.

Different types of t-tests in r

Type of T-Test Description Formula Use Case
Independent or Two-Sample T-Test Compares means of two independent groups to determine if they differ significantly. Also known as the between-subjects test. t = (mean1 - mean2) / sqrt((s1^2 / n1) + (s2^2 / n2)) Used when comparing two distinct and unrelated groups, e.g., assessing the impact of different treatments on separate groups.
Paired or Dependent T-Test Compares means of two related samples to assess if they differ significantly. Also used for pre-and post-intervention measurements on the same individuals. t = (mean of the differences) / (standard deviation of the differences/sqrt (n)) Appropriate for connected observations, like before-and-after measurements on the same subjects, such as in medical studies.
One-Sample T-Test Compares the mean of a single sample to a known or predicted value. Determines if the sample mean significantly differs from the expected value. t = (sample mean - hypothesized mean) / (sample standard deviation/sqrt (n)) Useful when evaluating if a sample mean significantly deviates from a predetermined value, e.g., comparing a sample mean to a population mean.

How to select the appropriate T-test

Understanding the t-test: Definition, Types, Formulas, and Applications" Slug: t-test-definition-types-formulas-applications

Paired t-test: When to use it and how it works

The paired t-test is used to compare two related samples. It computes the mean and standard deviation of the differences between the paired observations and then performs a one-sample t-test on the mean difference.

One-sample / Unpaired: Meaning and usage

The one-sample t-test is a statistical test used to compare a sample mean to a known or expected value. It compares the model mean to the anticipated value to see if the difference is statistically significant.

Paired vs. unpaired: Choosing the appropriate test

A paired t-test is used when observations are connected or related, such as pre- and post-treatment measures. An unpaired t-test is appropriate when the observations are unpaired and independent. The choice of test depends on the nature of the data and the study approach.

ANOVA vs. t-test: A comparison

ANOVA (Analysis of Variance) and t-tests compare means but in different conditions. ANOVA is used when comparing means across three or more groups or samples. Meanwhile, t-tests are appropriate for comparing means between two groups or samples.

How to perform t.test in R

  1. Define the hypothesis and the research question.
  2. Collect data from the two groups or samples of interest.
  3. Examine the assumptions of the t-test (normality, independence, and variance homogeneity).
  4. Calculate the test statistic (t-value) using the appropriate formula.
  5. Determine the degree of freedom and its crucial value.
  6. Calculate the p-value using the t-value and degrees of freedom (df).
  7. Compare the p-value to the desired significance level (e.g., 0.05) to determine the null hypothesis.
  8. Based on the results, make conclusions.

Assumptions and Sample Size Considerations

The following assumptions should be met to perform:

  • Random sample: Data should be collected using a random sample method.
  • Independence: The observations in the sample should be independent of one another.
  • Normality: The population distribution should be normally distributed.
  • Variance homogeneity: The variance of the populations under consideration should be the same.  
  • Larger sample sizes yield more accurate results and improve the result's capacity to detect statistically significant differences.

Assumption of T-test

To check the assumption of the t-test, read this comprehensive article, Parametric Tests in R : Your Guide to Powerful Statistical Analysis.

Perform t-test in R

R is a popular computer language for statistical analysis. R includes several methods and statistical analysis functions and packages. You can program and include these tools in your data analysis workflows. 

T.test Function

In R, the t.test function performs t-tests for comparing means. It takes two numeric vectors as input, conducting various types of t-tests, such as independent, paired, or one-sample tests. The function returns test statistics, p-values, and confidence intervals, aiding statistical inference and hypothesis testing; read more.

Load the data set

#T test Using iris data set
data(iris) ## Load the iris dataset
dim(iris) # dimension of the data
head(iris,10) # top ten rows of the data
			# Subset the data for the two species you want to compare
# In actual data set have three species for t test we are using only two.
# Subset the data set
setosa <- iris$Sepal.Length[iris$Species == "setosa"]
versicolor <- iris$Sepal.Length[iris$Species == "versicolor"]

How to perform an Independent t-test in R

The independent t-test compares the means of two groups to determine if they significantly differ. It assesses the impact of different treatments or conditions on unrelated groups, providing statistical evidence of significant differences in means; Read more.


Null hypothesis (H0): the mean sepal length of setosa species equals the mean sepal length of the versicolor species.

Alternative Hypothesis ( Ha): Setosa species have different mean sepal lengths than versicolor species.

Facing problems in formulating a hypothesis, here you can read a comprehensive article:  Hypothesis Test: Step-by-Step Guide for Students & Researchers.

Perform independent t-test in R.

t.test(setosa, versicolor)

Perform independent t-test in R by using the t.test function Interpretation of t-test results

The independent t-test has a t-value of -10.521 and a degree of freedom (df) of 86.538. The p-value is incredibly low (2.2e-16), much below the commonly used significance level of 0.05. As a result, the null hypothesis is rejected.

These results support the alternative hypothesis. It implies that the mean sepal length of iris flowers from the species setosa and versicolor differs statistically considerably. Setosa species have 5.006 average sepal lengths, while versicolor species have 5.936 average sepal lengths.

Furthermore, the 95% confidence interval (-0.7542926, -1.1057074) eliminates 0. These values represent a range of plausible values for the actual difference in means. The absence of 0 confirms that the sepal lengths of the two species differ significantly.

How to perform a Paired t-test


  • Null hypothesis ( H0): the mean difference in sepal lengths between setosa and versicolor species equals zero.
  • Alternative hypothesis ( Ha), The mean difference in sepal lengths between setosa and versicolor species is greater than zero.

Paired sample t.test in R

# Perform paired t-test
t.test(setosa, versicolor, paired = TRUE)

permfrom a paired sample t.test in R by using the t.test function Interpretation of paired sample t-test

A paired t-test result of -10.146 and a degree of freedom (df) of 49 is obtained. The p-value is 1.242e-13, which is very little. The null hypothesis is rejected because the p-value is less than the commonly used significance level of 0.05.

It demonstrates that there is substantial evidence to support the alternative theory. It exhibits statistically significant differences in the mean sepal length of iris flowers from the setosa and versicolor species. The negative mean difference of -0.93 indicates that versicolor species have shorter sepal lengths on average than setosa species.

Furthermore, the 95% confidence interval (CI) (-1.114203, -0.745797) has no zeros. CI provides a range of values for the true mean difference. The absence of 0 confirms the conclusion that the sepal lengths of the two species differ significantly.

Based on the results, we may say that strong evidence supports the alternative hypothesis. Because results suggest that iris flowers' mean sepal length differs significantly between the setosa and versicolor species.

How to perform One sample t-test in R

The one-sample t-test evaluates whether the mean of a single sample significantly deviates from a known or predicted value. It assesses if observed data substantially differs from a specified expectation, aiding researchers in making statistically supported conclusions about the population mean based on a representative sample.


  • Null hypothesis ( H0): the population's mean sepal length is 5.5.
  • Alternative hypothesis ( Ha): The mean sepal length of the population is not 5.5.

Perform one Sample t-test

# Perfrom one sample t test
t.test(iris$Sepal.Length, mu = 5.5)

One sample t-test by using the t.test function of Rstudio Interpretation of one Sample t-test

The test calculations are 5.078 and a degree of freedom (df) of 149. The p-value is 1.123e-06, which is very small. We reject the null hypothesis because the p-value is less than the commonly used significance level of 0.05.

The results significantly support the alternative hypothesis. It means that there is a statistically significant difference between the mean sepal length of the population. The hypothesized value is 5.5, and the sample's mean sepal length (mean of x) is 5.843333.

Furthermore, the 95% confidence interval (5.709732, 5.976934) contains no 5.5. The absence of 5.5 validates the conclusion that there is a substantial gap between the mean and postulated sepal length. 

What is the Significance level?

The significance level (alpha) is a criterion for determining statistical significance. When the null hypothesis is true, it can be rejected (Type I error). 

The most frequent significance level is 0.05. It indicates that the null hypothesis is rejected if the p-value is less than 0.05. The significance threshold, however, can be adjusted depending on the unique study environment and desired level of assurance.

Problems with solutions

Consider the following problems and solutions to gain a better understanding:

Problem: Is there a big difference in the average earnings of employees at two different companies?

Solution: Conduct an unbiased t-test on employee earnings data from both firms.

Problem: Does a new teaching method considerably improve students' test scores?

Solution: Use a paired t-test to compare students' test scores before and after implementing the new teaching method.


Finally, the t-test is a useful statistical tool for comparing mean values across groups or samples. It allows researchers to assess the significance of observed differences and form conclusions about the populations they represent. If researchers understand the types of tests, formulas, assumptions, and applications. They can reliably assess data and derive relevant conclusions.

The t-test is helpful in a wide range of disciplines, including psychology, biology, medicine, economics, and social sciences. It helps researchers evaluate the effectiveness of interventions, compare group means, and investigate research problems. However, to achieve reliable and valid findings, it is necessary to ensure that the t-test assumptions are met.

Frequently Asked Questions (FAQs)

Why is it called a t-test and used for?

To determine whether or not there is a significant difference between the means of two groups or samples. When the data have a normal distribution, and the populations' variances arither the same or different, it is applied relatively frequently in the data analysis.

What are the three main types of t-tests and their differences?

Three main types of tests are Independent, Paired, and one sample test.

What is the difference between a t-test and an ANOVA?

While both tests are used to compare means, and the differences are:T-test It reaches means between two groups or samples. ANOVA: It compares means between multiple groups or samples simultaneously. It assesses if there are any significant differences among the means of the groups but does not identify which specific group means differ from each other.

What does the t-test value tell you, and what are the T score, t-value, and p-value?

A statistic, the t-test value, is used to evaluate the degree of disparity between the means of two groups concerning the amount of variance within the groups.

What is the minimum sample size for a t-test, and is parametric or nonparametric?

The required power of the test, the effect size, and the significance level are some criteria that can influence the minimum sample size that should be used. Generally, a bigger sample size yields more accurate results. The t-test is a parametric test, which means it makes specific assumptions about the population distribution, such as that it is normal and the variances are all the same.

How do you analyze t-test results and interpret them?

When analyzing results, looking at the determined t-value and the corresponding p-value is common practice. If the p-value is lower than the significance level you have set (for example, 0.05), this indicates a statistically significant difference between the two means. You would conclude that there is evidence of a substantial difference and reject the hypothesis that there is no change. However, you cannot reject the null hypothesis if the p-value exceeds the significance level. This indicates that there is not enough evidence to conclude that there is a significant difference.

Why do we reject the null hypothesis in a t-test, and how do we do it?

When the p-value is lower than the significance level that we've established, we disregard the null hypothesis as invalid. This suggests that the discrepancy in the means could not have resulted from pure random chance alone; instead, it is highly implausible. There is adequate evidence to suggest a significant difference between the groups after comparing the p-value to the significance level (for example, 0.05). This allows us to conclude that the null hypothesis should be rejected.

What is a good score in a t-test, and what does 95% represent for a T score?

The setting and the particular research topic both play a role in determining what constitutes a good score on a t-test. In most cases, a meaningful difference between the groups being compared can be inferred from a significant t-score. A T score with a confidence level of 95% indicates that if the experiment were to be Using a car dataset, we can illustrate the application of the two sample t-test, assuming unequal variances, where welch or pooled variance solutions could be considered.ried out more than once, we would anticipate that 95% of the calculated T scores would fall within the critical region, resulting in the rejection of the null hypothesis.

Do you need help with a data analysis project? Let me assist you! With a PhD and ten years of experience, I specialize in solving data analysis challenges using R and other advanced tools. Reach out to me for personalized solutions tailored to your needs.

About the Author

Ph.D. Scholar | Certified Data Analyst | Blogger | Completed 5000+ data projects | Passionate about unravelling insights through data.

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