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What is the syntax for t test in R?
The basic syntax for t.test () is: t.test (x, y = NULL, mu = 0, var.equal = FALSE) arguments: – x : A vector to compute the one-sample t-test – y: A second vector to compute the two sample t-test – mu: Mean of the population- var.equal: Specify if the variance of the two vectors are equal.
Which is an example of a paired t test?
A t-test can be estimated for: Two vectors from the same sample group (i.e., paired t-test). You assume that both vectors are randomly sampled, independent and come from a normally distributed population with unknown but equal variances.
When to use the t test in statistics?
The t-test belongs to the family of inferential statistics. It is commonly employed to find out if there is a statistical difference between the means of two groups. If we assume the variances are equal, we need to change the parameter var.equal= TRUE.
What are the assumptions in the t test?
Remember, one assumption in the t-test is an unknown but equal variance. In reality, the data barely have equal mean, and it leads to incorrect results for the t-test. One solution to relax the equal variance assumption is to use the Welch’s test. R assumes the two variances are not equal by default.
What do you need to know about two sample t test?
• Two-sample data. That is, one measurement variable in two groups or samples • Independent variable is a factor with two levels. That is, two groups • For Student’s t -test, the two samples need to have the same variance. However, Welch’s t -test, which is used by default in R, does not assume equal variances.
What is the p-value of one sample t test?
The p-value of the one sample t-test is 0.1079 and above 0.05. You can be confident at 95% that the amount of sugar added by the machine is between 9.973 and 10.002 grams. You cannot reject the null (H0) hypothesis.