Two-Sample t Test in R (Independent Groups) with Example | R Tutorial 4.2 | MarinStatsLectures

Two-Sample t Test in R (Independent Groups) with Example: Learn how to conduct the independent two-sample t-test and calculate confidence interval with R Statistical Software; 👉🏼Related: Two Sample t Test in Statistics Video    • Two Sample t-test for Independent Gro...   📝 R practice dataset (LungCapData): https://statslectures.com/r-scripts-d...
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In this R video tutorial, we will learn how to conduct the independent two-sample t-test and confidence interval for the difference in means of two populations. We will use various R functions and arguments to perform two sample t-test such as "t.test" function, "boxplot", "var.eq" and "leveneTest" and more.

Independent two sample t-test and confidence interval are parametric methods appropriate for examining the difference in means for two populations. These are ways of examining the relationship between a numeric outcome variable (Y variable) and a categorical explanatory variable (X variable with 2 levels)


Table of Content:

0:00:10 when should we use the independent two sample t-test and confidence interval in statistics and in research
0:00:53 how to access the help menu in R for t-test
0:01:04 how to visually examine the relationship between two variables in R
0:01:18 how to conduct an independent two-sided t-test with non-equal population variances in R using the "t.test" function
0:01:56 introducing the null and alternative hypothesis, the confidence interval, and variance assumption with example
0:03:06 how to use the "mu" argument in two-sided t-test
0:03:12 how to use the "alt" argument to do a one-sided t-test
0:03:18 how to use the "conf" argument to change the confidence level for the t-test
0:03:24 how to use the "var.eq" argument to assume equal population variances for t-test
0:03:29 how to let R know that groups are paired or dependent using the "paired" argument
0:03:37 two different ways for separating the groups in "t.test" command/function in R
0:04:18 how to decide if we should assume equal or non-equal variances using boxplot
0:04:38 how to decide if we should assume equal or non equal variances comparing the actual variances
0:05:02 how to test the null hypothesis "that the population variances are equal" using Levene's test using "leveneTest" function


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