# FAQ: How To Make A Decision On A Two Sample T Test?

## How do you interpret a two tailed t test?

A two-tailed test will test both if the mean is significantly greater than x and if the mean significantly less than x. The mean is considered significantly different from x if the test statistic is in the top 2.5% or bottom 2.5% of its probability distribution, resulting in a p-value less than 0.05.

## How do you know if two samples are statistically different?

3.2 How to test for differences between samples

1. Decide on a hypothesis to test, often called the “null hypothesis” (H0 ).
2. Decide on a statistic to test the truth of the null hypothesis.
3. Calculate the statistic.
4. Compare it to a reference value to establish significance, the P-value.

## What is the null hypothesis for a two-sample t test?

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.

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## What is p-value in hypothesis testing?

In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

## What are the two rejection areas in using a two-tailed test and the 0.01 level of significance?

The rejection region is in both the upper and lower tails of the distribution. What are the critical values for a two-tailed test with a 0.01 level of significance when n is large and the population standard deviation is known?

## How do you know if two samples are independent?

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

## What is the p-value in a 2 sample t-test?

The p-value is the probability that the difference between the sample means is at least as large as what has been observed, under the assumption that the population means are equal.

## How do you compare two-sample means?

The four major ways of comparing means from data that is assumed to be normally distributed are:

1. Independent Samples T-Test.
2. One sample T-Test.
3. Paired Samples T-Test.
4. One way Analysis of Variance (ANOVA).

## 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.

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## What are the assumptions for a two-sample t-test?

Two-sample t-test assumptions 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 null hypothesis for a paired t-test?

Our null hypothesis is that the mean difference between the paired exam scores is zero. Our alternative hypothesis is that the mean difference is not equal to zero. The software shows a p-value of 0.4650 for the two-sided test.

## What does p-value.05 mean?

P > 0.05 is the probability that the null hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.

## What does p-value of 1 mean?

Popular Answers (1) When the data is perfectly described by the resticted model, the probability to get data that is less well described is 1. For instance, if the sample means in two groups are identical, the p-values of a t-test is 1.

## What is p-value formula?

The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test). The p-value for: an upper-tailed test is specified by: p-value = P(TS ts | H is true) = 1 – cdf(ts)