# Examples of t test research questions

## What is a one-sample t-test example?

A one sample test of means compares the mean of a sample to a pre-specified value and tests for a deviation from that value. For example we might know that

**the average birth weight for white babies in the US is 3,410 grams and wish to compare the average birth weight of a sample of black babies to this value**.## What is Student t-test explain with an example?

**It lets you know if those differences in means could have happened by chance**. The t test is usually used when data sets follow a normal distribution but you don’t know the population variance. For example, you might flip a coin 1,000 times and find the number of heads follows a normal distribution for all trials.

## What type of research uses t-test?

The t test is one type of inferential statistics. It is used to determine whether there is a significant difference between the means of two groups. With all inferential statistics, we assume the dependent variable fits a normal distribution.

## What is an example of an independent t-test?

For example, you could use an independent t-test

**to understand whether first year graduate salaries differed based on gender**(i.e., your dependent variable would be “first year graduate salaries” and your independent variable would be “gender”, which has two groups: “male” and “female”).## What are the 3 types of t tests?

There are three t-tests to compare means:

**a one-sample t-test, a two-sample t-test and a paired t-test**.## Is t-test qualitative or quantitative?

quantitative

ANOVA and t-tests are statistical tests for significance and therefore

**quantitative**.## What are the 4 types of t-tests?

**Types of t-tests (with Solved Examples in R)**

- One sample t-test.
- Independent two-sample t-test.
- Paired sample t-test.

## What is the t-test null hypothesis?

The default null hypothesis for a 2-sample t-test is that the two groups are equal. You can see in the equation that when the two groups are equal, the difference (and the entire ratio) also equals zero.

## What is the difference between one sample and two sample t-test?

The 2-sample t-test takes your sample data from two groups and boils it down to the t-value. The process is very similar to the 1-sample t-test, and you can still use the analogy of the signal-to-noise ratio. Unlike the paired t-test,

**the 2-sample t-test requires independent groups for each sample**.## What is the hypothesis for a one-sample t-test?

The null hypothesis for a one sample t test can be stated as: “

**The population mean equals the specified mean value**.” The alternative hypothesis for a one sample t test can be stated as: “The population mean is different from the specified mean value.”## What is the difference between one-tailed and two-tailed t-test?

**One-tailed tests allow for the possibility of an effect in one direction.**

**Two-tailed tests test for the possibility of an effect in two directionsâ€”positive and negative**.

## How do you know if it is a one-tailed or two-tailed test?

How can we tell whether it is a one-tailed or a two-tailed test? It depends on the original claim in the question.

**A one-tailed test looks for an â€śincreaseâ€ť or â€śdecreaseâ€ť in the parameter whereas a two-tailed test looks for a â€śchangeâ€ť (could be increase or decrease) in the parameter**.## When should you use the t-test?

When to use a t-test. A t-test can only be used

**when comparing the means of two groups**(a.k.a. pairwise comparison). If you want to compare more than two groups, or if you want to do multiple pairwise comparisons, use an ANOVA test or a post-hoc test.## Does t-test require population mean?

**A t-test is an analysis of two population means through the use of statistical examination**; a t-test with two samples is commonly used with small sample sizes, testing the difference between the samples when the variances of two normal distributions are not known.

## What is the minimum sample size for t-test?

**There is no minimum sample size required to perform a t-test**. In fact, the first t-test ever performed only used a sample size of four. However, if the assumptions of a t-test are not met then the results could be unreliable.

## How do you analyze t-test results?

**Compare the P-value to the Î± significance level stated earlier.**

**If it is less than Î±, reject the null hypothesis.**

**If the result is greater than Î±, fail to reject the null hypothesis**. If you reject the null hypothesis, this implies that your alternative hypothesis is correct, and that the data is significant.

## How do you present t-test results?

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 are the 4 types of t tests?

**Types of t-tests (with Solved Examples in R)**

- One sample t-test.
- Independent two-sample t-test.
- Paired sample t-test.

## How do you know if t-value is significant?

**If the computed t-score equals or exceeds the value of t indicated in the table**, then the researcher can conclude that there is a statistically significant probability that the relationship between the two variables exists and is not due to chance, and reject the null hypothesis.

## How do you analyze a one sample t test?

**Quick Steps**

- Analyze -> Compare Means -> One-Sample T Test.
- Drag and drop the variable you want to test against the population mean into the Test Variable(s) box.
- Specify your population mean in the Test Value box.
- Click OK.
- Your result will appear in the SPSS output viewer.