t test calculator A t test compares the means of two groups. For example, compare whether systolic blood pressure differs between a control and treated group, . Further Information. A t-test is used when you're looking at a numerical variable - for example, height - and then comparing the averages of two separate populations or groups (e.g., males and females).
The t-test is used to find out if the means between two populations is significantly different. Characteristics of the test are; 1 The test statistic follows a t distribution under null hypothesis.
Note that the two populations need to follow the normal distribution. Also the variances of the two populations need to be equal if sample size is less than A test of type 3 is a paired test. The samples are independent. A test of type 4 is an unpaired test. In many cases of unpaired data, it is the same variable undergoing repeated observations.
For example, measurements taken before and after an experiment. F-test is used to find out if the variances between the two populations are significantly different. Characteristics of an F-test are: ANOVA 3 F-test can be used to find out if the data fits into a regression model obtained using least square analysis.
I have a sample dataset with 31 values. I ran a two-tailed t-test using R to test if the true mean is equal to The t-value calculated using this method is the same as output by the t-test R function. The p-value, however, comes out to be 3. I posted this as a comment but when I wanted to add a bit more in edit, it became too long so I've moved it down here. Your test statistic and d. The other answer notes the issue with the calculation of the tail area in the call to pt , and the doubling for two-tails, which resolves your difference.
It's possible you could be doing nothing wrong and still get a difference, but if you post a reproducible example it might be possible to investigate further whether you have some error say in the df. These things are calculated from approximations that may not be particularly accurate in the very extreme tail.
If the two things don't use identical approximations they may not agree closely, but that lack of agreement shouldn't matter for the exact tail area out that far to be meaningful number, the required assumptions would have to hold to astounding degrees of accuracy. Do you really have exact normality, exact independence, exactly constant variance? You shouldn't necessarily expect great accuracy out where the numbers won't mean anything anyway.
Any ideas what I'm doing wrong?
A test of type 3 is a paired test. It's possible you could be doing nothing wrong and still get a difference, but if you post a reproducible example it might be possible to investigate further whether you have some error say in the df.
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