- How do you know if a t test is significant?
- Do I want a high or low P value?
- Why are my p values so high?
- What is a critical value in statistics?
- How do you know when to reject or fail to reject?
- How do you know when to reject the null?
- What is T value and p value?
- Is a higher T value better?
- What does it mean if the t test shows that the results are not statistically significant?
- What is a good t value?
- Can P values be greater than 1?
- When T value is significant?
- What is a high T value?
- What does P value indicate?
- What does P value of 1 mean?
- How do you find t critical value?
- What does it mean if the T value is greater than the critical value?
- What does t test tell you?

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

Compare the P-value to the α significance level stated earlier.

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..

## Do I want a high or low P value?

P values evaluate how well the sample data support the devil’s advocate argument that the null hypothesis is true. It measures how compatible your data are with the null hypothesis. … High P values: your data are likely with a true null. Low P values: your data are unlikely with a true null.

## Why are my p values so high?

High p-values indicate that your evidence is not strong enough to suggest an effect exists in the population. An effect might exist but it’s possible that the effect size is too small, the sample size is too small, or there is too much variability for the hypothesis test to detect it.

## What is a critical value in statistics?

In hypothesis testing, a critical value is a point on the test distribution that is compared to the test statistic to determine whether to reject the null hypothesis. If the absolute value of your test statistic is greater than the critical value, you can declare statistical significance and reject the null hypothesis.

## How do you know when to reject or fail to reject?

Remember that the decision to reject the null hypothesis (H 0) or fail to reject it can be based on the p-value and your chosen significance level (also called α). If the p-value is less than or equal to α, you reject H 0; if it is greater than α, you fail to reject H 0.

## How do you know when to reject the null?

After you perform a hypothesis test, there are only two possible outcomes.When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis. … When your p-value is greater than your significance level, you fail to reject the null hypothesis.

## What is T value and p value?

A nice definition of p-value is “the probability of observing a test statistic at least as large as the one calculated assuming the null hypothesis is true”. … Now, I assume that what you’re calling “t-value” is a generic “test statistic”, not a value from a “t distribution”.

## Is a higher T value better?

Generally, any t-value greater than +2 or less than – 2 is acceptable. The higher the t-value, the greater the confidence we have in the coefficient as a predictor. Low t-values are indications of low reliability of the predictive power of that coefficient.

## What does it mean if the t test shows that the results are not statistically significant?

This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).

## What is a good t value?

T-tests are called t-tests because the test results are all based on t-values. … A t-value of 0 indicates that the sample results exactly equal the null hypothesis. As the difference between the sample data and the null hypothesis increases, the absolute value of the t-value increases.

## Can P values be greater than 1?

P values should not be greater than 1. They will mean probabilities greater than 100 percent.

## When T value is significant?

So if your sample size is big enough you can say that a t value is significant if the absolute t value is higher or equal to 1.96, meaning |t|≥1.96.

## What is a high T value?

Higher values of the t-value, also called t-score, indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets. A large t-score indicates that the groups are different. A small t-score indicates that the groups are similar.

## What does P value indicate?

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 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.

## How do you find t critical value?

To find a critical value, look up your confidence level in the bottom row of the table; this tells you which column of the t-table you need. Intersect this column with the row for your df (degrees of freedom). The number you see is the critical value (or the t*-value) for your confidence interval.

## What does it mean if the T value is greater than the critical value?

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 does t test tell you?

The t test tells you how significant the differences between groups are; In other words it lets you know if those differences (measured in means) could have happened by chance. … A t test can tell you by comparing the means of the two groups and letting you know the probability of those results happening by chance.