Search results
One sample t-test. Choose the one-sample t-test to check if the mean of a population is equal to some pre-set hypothesized value. Examples: The average volume of a drink sold in 0.33 l cans — is it really equal to 330 ml? The average weight of people from a specific city — is it different from the national average? Two-sample t-test
- P-value
Welcome to our p-value calculator! You will never again have...
- Mann–Whitney U Test
This Mann-Whitney U test calculator is here to help whenever...
- Z-test Calculator
The critical value approach involves comparing the value of...
- Normal Distribution
You can use our normal distribution probability calculator...
- P-value
In this one, you’ll understand when to use the T-Test, the different types of T-Test, math behind it, how to determine which test to choose in what situation and why, how to read from the t-tables, example situations and how to apply it in R and Python.
31 sty 2020 · A t-test is a statistical test that compares the means of two samples. It is used in hypothesis testing, with a null hypothesis that the difference in group means is zero and an alternate hypothesis that the difference in group means is different from zero.
27 kwi 2023 · Assuming for the moment that you want to run a two-sided test, the goal is to determine whether two “independent samples” of data are drawn from populations with the same mean (the null hypothesis) or different means (the alternative hypothesis).
28 sty 2020 · If your data do not meet the assumption of independence of observations, you may be able to use a test that accounts for structure in your data (repeated-measures tests or tests that include blocking variables).
The independent t-test formula is also referred as: unpaired t-test formula, independent samples t-test formula, two sample t-test formula, 2 sample t-test formula and. two sample t-test equation. The independent samples t-test comes in two different forms: the standard Student’s t-test, which assumes that the variance of the two groups are equal.
There is an independent samples t-test (this example) that compares two samples to each other. There is a paired data (also called correlated data) t-test that compares two samples from data that is related (like pretest score and post test score).
