Contents

- 1 Under what circumstances might it be more appropriate to use a Welch’s t test instead of a Student’s t test?
- 2 What are the conditions for t test?
- 3 What is unpaired t test with Welch’s correction?
- 4 What is Welch’s correction?
- 5 Does data need to be normal for t-test?
- 6 How do you know if variances are equal or unequal?
- 7 How do you reject the null hypothesis in t test?
- 8 What are the conditions for a 2 sample t test?
- 9 What is the difference between z and t test?
- 10 What if variances are not equal?
- 11 How do I report a t test?
- 12 What is the paired t test?
- 13 What does a negative T value mean?
- 14 Why do we use one sample t test?
- 15 What does the Welch’s t test assume?

## Under what circumstances might it be more appropriate to use a Welch’s t test instead of a Student’s t test?

Take home message of this post: We should **use Welch’s t**–**test** by default, **instead** of **Student’s t**–**test**, because **Welch’s t**–**test** performs better than **Student’s t**–**test** whenever sample sizes and variances are unequal between groups, and gives the same result **when** sample sizes and variances are equal.

## What are the conditions for t test?

The common assumptions made when doing a **t**–**test** include those regarding the scale of measurement, random sampling, normality of data distribution, adequacy of sample size and equality of variance in standard deviation.

## What is unpaired t test with Welch’s correction?

Two **unpaired t tests**

Use the unequal variance **t test**, also called the **Welch t test**. It assues that both groups of data are sampled from Gaussian populations, but does not assume those two populations have the same standard deviation.

## What is Welch’s correction?

**Welch’s** Test for Unequal Variances (also called **Welch’s** t-test, **Welch’s** adjusted T or unequal variances t-test) is a modification of Student’s t-test to see if two sample means are significantly different. The null hypothesis for the test is that the means are equal.

## Does data need to be normal for t-test?

For a **t**–**test** to be valid on a sample of smaller size, the population distribution would have to be approximately **normal**. The **t**–**test** is invalid for small samples from non-**normal** distributions, but it is valid for large samples from non-**normal** distributions.

## How do you know if variances are equal or unequal?

An F-**test** (Snedecor and Cochran, 1983) is used to **test if** the **variances** of two populations are **equal**. This **test** can be a two-tailed **test** or a one-tailed **test**. The two-tailed version tests against the alternative that the **variances** are **not equal**.

## How do you reject the null hypothesis in t test?

If the absolute value of the **t**-value is greater than the critical value, you **reject** the **null hypothesis**. If the absolute value of the **t**-value is less than the critical value, you fail to **reject** the **null hypothesis**.

## What are the conditions for a 2 sample t test?

**Two- sample t–test assumptions**

- Data values must be independent.
- Data in each group must be obtained via a random
**sample**from the population. - Data in each group are normally distributed.
- Data values are continuous.
- The variances for the two independent groups are equal.

## What is the difference between z and t test?

**Z**–**tests** are statistical calculations that can be used to **compare** population means to a sample’s. **T**–**tests** are calculations used to **test** a hypothesis, but they are most useful when we need to determine if there is a statistically significant **difference between** two independent sample groups.

## What if variances are not equal?

When the **variances** across groups are **not equal**, the usual assumptions for analysis of **variance are not** satisfied. Therefore, the ANOVA F test is **not** valid. JMP provides four tests for **equality** of group **variances** and an ANOVA that is valid when the group population **variances are unequal**.

## How do I report a t test?

The basic format for **reporting** the result of a **t**–**test** is the same in each case (the color red means you substitute in the appropriate value from your study): **t**(degress of freedom) = the **t** statistic, p = p value. It’s the context you provide when **reporting** the result that tells the reader which type of **t**–**test** was used.

## What is the paired t test?

A **paired t**–**test** is used when we are interested in the difference between two variables for the same subject. Often the two variables are separated by time. Since we are ultimately concerned with the difference between two measures in one sample, the **paired t**–**test** reduces to the one sample **t**–**test**.

## What does a negative T value mean?

Find a **t**–**value** by dividing the difference between group **means** by the standard error of difference between the groups. A **negative t**–**value** indicates a reversal in the directionality of the effect, which has no bearing on the significance of the difference between groups.

## Why do we use one sample t test?

The **one**–**sample t**–**test** is a statistical hypothesis **test used** to determine whether an unknown population mean is different from a specific value.

## What does the Welch’s t test assume?

Student’s **t**–**test assumes** that the sample **means** (**test** statistics) of two population distributions being compared **are** normally distributed with equal variance. **Welch’s t**–**test is** designed for unequal sample distribution variance, but the assumption of sample distribution normality **is** maintained.