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Performs unpaired t test, Weldh's t test (doesn't assume equal variances) and paired t test. Calculates exact P value and 95% confidence interval. Clear results with links to extensive explanations.
This example illustrates how each type of t test could be chosen for a specific analysis, and why the one sample t test is the correct choice to determine if the measured pH of the bottled water samples match the advertised pH of 8.5.
If you’re wondering how to do a t test, the easiest way is with statistical software such as Prism or an online t test calculator. If you’re using software, then all you need to know is which t test is appropriate ( use the workflow here ) and understand how to interpret the output.
Descriptive statistics, detect outlier, t test, CI of mean / difference / ratio / SD, multiple comparisons tests, linear regression.
This calculator is set up for you to enter data in a format that is natural to many scientists. For example, to compare the blood pressure of a group of men and a group of women, enter the men's blood pressure in one column and the women's blood pressure in another.
Calculate P from t, z, r, F or chi-square, or vice-versa. View Binomial, Poisson or Gaussian distribution. Correct a P value for multiple comparisons and Bayes.
This calculator is for 2x2 contingency tables that separate each subject into one of four categories based on two factors, each with two possibilities. Simply label the rows and columns, then type in the counts for each cell to test the relationship between the two factors.
The t test compares the difference between two means and compares that difference to the standard error of the difference, computed from the standard deviations and sample size. If you only know the two means, there is no possible way to do any statistical comparison.
Use this calculator to compute a two-tailed P value from any Z score, T score, F statistic, correlation coefficient (R), or chi-square value. Once you have obtained one of these statistics (from a publication or even another program) the P value helps interpret its statistical significance.
Use the binomial test when there are two possible outcomes. You know how many of each kind of outcome (traditionally called "success" and "failure") occurred in your experiment. You also have a hypothesis for what the true overall probability of "success" is.