For example, let’s assume the nation-wide average of college educated adults is 32% (Bachelor’s degree or higher) and we want to see if the midwest mean is significantly different than the national average in particular we want to test if the midwest average is less than the national average.One-sample t-test using Minitab Introduction Consider we want to assess the percent of college educated adults in the midwest and compare it to a certain value. The one-sample t-test compares a sample’s mean with a known value, when the variance of the population is unknown. The wilcox.test() function provides the same basic functionality and arguments however, wilcox.test() is used when we do not want to assume the data to follow a normal distribution. The default assumes unequal variance and applies the Welsh approximation to the degrees of freedom however, you can set this to TRUE to pool the variance.įinally, the conf.level argument determines the confidence level of the reported confidence interval for in the one-sample case and in the two-sample case. The var.equal argument indicates whether or not to assume equal variances when performing a two-sample t-test. The default is set to FALSE but can be set to TRUE if you desire to perform a paired t-test. The paired argument will indicate whether or not you want a paired t-test. …performs a one-sample t-test on the data contained in x where the null hypothesis is that and the alternative is that. T.test ( x, alternative = "less", mu = 25 ) The function contains a variety of arguments and is called as follows:
The t.test() function can be used to perform both one and two sample t-tests on vectors of data. with 20 more variables: popamerindian, popasian, # popother, percwhite, percblack, percamerindan, # percasian, percother, popadults, perchsd, # percollege, percprof, poppovertyknown, # percpovertyknown, percbelowpoverty, # percchildbelowpovert, percadultpoverty, # percelderlypoverty, inmetro, category I also use the dplyr, tidyr, magrittr, and gridExtra packages. For the paired t-test I willustrate with the built in sleep data set. This tutorial leverages the midwest data that is provided by the ggplot2 package for the one and two-sample independent t-tests. Paired t-tests: Compare the means of two sets of paired samples, taken from two populations with unknown variance.Two-sample t-tests: Compare the means of two groups under the assumption that both samples are random, independent, and normally distributed with unknown but equal variances.One-sample t-tests: Compare the sample mean with a known value, when the variance of the population is unknown.t.test() & wilcox.test(): The basic functions you’ll leverage for the various t-tests.Replication requirements: What you’ll need to reproduce the analysis in this tutorial.First, I provide the data and packages required to replicate the analysis and then I walk through the basic operations to perform t-tests. This tutorial serves as an introduction to performing t-tests to compare two groups. This tutorial covers the basics of performing t-tests in R. There is also a widely used modification of the t-test, known as Welch’s t-test that adjusts the number of degrees of freedom when the variances are thought not to be equal to each other. It is known that under the null hypothesis, we can calculate a t-statistic that will follow a t-distribution with degrees of freedom. The null hypothesis is that the two means are equal, and the alternative is that they are not. The assumption for the test is that both groups are sampled from normal distributions with equal variances. One of the most common tests in statistics, the t-test, is used to determine whether the means of two groups are equal to each other.