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I want to test a hypothesis, where i want to test the marks scored by Boys and girls.
Gender = 1 is for males and gender = 0 is for females.
assuming I am reading a data from a CSV file this is how I am representing it.
hp<-read.csv("hyp.csv")
t.test(hp$Math[hp$Gender==1],mu=mean(hp$Math[hp$Gender==0]),alternative ="greater" ))
Is this the correct representation of the code. I am putting the girls mean in mu and boys as X.
Any help will be appreciated.
Thanks and regards,
Aditya
Try the formula interface to t.test like so:
t.test(hp$Math ~ hp$Gender, alternative = "greater")
this will perform a Welch corrected t-test for independent groups. However, it is almost never justified to test alternative = "greater". In most instances by far, you should test alternative="two.sided" which is standard for t.test, if you do not specify alternative.
The formula interface (using ~) is shorter and therefore more readable and less error prone whenever the data comes in this form.
Use mu = if you want to perform a one sample test, e. g. compare female's grade to a fixed value like 2.5.
Finally: People at CrossValidated recognize questions on R syntax as off Topic (R Syntax is not statistics). Consider asking questions like this one on StackOverflow.
There are two issues here, one about t-test and another about coding in R.
You don't have one sample from one population: you have two samples from two populations, and you are testing if means of both populations are equal. Therefore, you have to perform a two sample t-test.
About coding, you are using "hp" as variable, but "hp" is a data frame, and you need a single variable, that is one column of hp.
If scores are in a column named "score" code should be as following:
The code should be:
t.test(x=hp$score[Gender==1],y=hp$score[Gender==0],alt="greater")
