## What is the independent sample t-test?

The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test.

## What is the difference between a dependent and independent samples t-test?

Dependent samples are paired measurements for one set of items. Independent samples are measurements made on two different sets of items. If the values in one sample reveal no information about those of the other sample, then the samples are independent.

## What is an example of an independent sample?

For example to compare heights of males and females, we could take a random sample of 100 females and another random sample of 100 males. The result would be two samples which are independent of each other.

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## What is the T value in an independent t-test?

When you perform a t-test, you’re usually trying to find evidence of a significant difference between population means (2-sample t) or between the population mean and a hypothesized value (1-sample t). The t-value measures the size of the difference relative to the variation in your sample data.

## What is the formula for independent t-test?

In the case of a t-test, there are two samples, so the degrees of freedom are N1 + N2 – 2 = df. Once you determine the significance level (first row) and the degrees of freedom (first column), the intersection of the two in the chart is the critical value for your particular study.

## What is the difference between the t-test for two independent samples and the t-test for two dependent samples eg matched pairs t-test?)?

Two-sample t-test is used when the data of two samples are statistically independent, while the paired t-test is used when data is in the form of matched pairs. To use the two-sample t-test, we need to assume that the data from both samples are normally distributed and they have the same variances.

## What are the assumptions of the independent sample 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 p value in t-test?

T-Values and P-values A p-value is the probability that the results from your sample data occurred by chance. P-values are from 0% to 100%. 01 means there is only a 1% probability that the results from an experiment happened by chance. In most cases, a p-value of 0.05 (5%) is accepted to mean the data is valid.

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## How do you know if a t-test is significant?

If it is less than α, reject the null hypothesis. If the result is greater than α, 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 independent 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 is a independent sample?

Learn more about Minitab 19. Independent samples are samples that are selected randomly so that its observations do not depend on the values other observations. Many statistical analyses are based on the assumption that samples are independent. Others are designed to assess samples that are not independent.

## What is the difference between t-test and Z test?

T-test refers to a type of parametric test that is applied to identify, how the means of two sets of data differ from one another when variance is not given. Z-test implies a hypothesis test which ascertains if the means of two datasets are different from each other when variance is given.

## How do you find two independent samples?

The test statistic for a two-sample independent t-test is calculated by taking the difference in the two sample means and dividing by either the pooled or unpooled estimated standard error. The estimated standard error is an aggregate measure of the amount of variation in both groups.