Contents

- 1 What will be the decision if the T critical value is greater than the T computed value?
- 2 What does it mean if the T value is greater than the critical value?
- 3 What happens when the computed statistical value is greater than the positive critical value?
- 4 What will be your decision if the p value is less than the level of significance used?
- 5 What is considered a high T value?
- 6 How do you reject the null hypothesis with p-value?
- 7 Is a higher T value better?
- 8 How do you know when to reject the null?
- 9 How do you know when to reject or fail to reject?
- 10 What is the critical value at the 0.05 level of significance?
- 11 What is the critical value at the 0.01 level of significance?
- 12 What is a positive critical value?
- 13 Is P value 0.1 Significant?
- 14 Why reject null hypothesis when p value is small?
- 15 What do p values tell us?

## What will be the decision if the T critical value is greater than the T computed value?

If the absolute value of the calculated t – statistic is larger than the critical value of t, we reject the null hypothesis.

## 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 happens when the computed statistical value is greater than the positive critical value?

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.

## What will be your decision if the p value is less than the level of significance used?

If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis.

## What is considered a high T value?

The greater the magnitude of T, the greater the evidence against the null hypothesis. This means there is greater evidence that there is a significant difference. The closer T is to 0, the more likely there isn’ t a significant difference.

## How do you reject the null hypothesis with p-value?

If the p – value is less than 0.05, we reject the null hypothesis that there’s no difference between the means and conclude that a significant difference does exist. If the p – value is larger than 0.05, we cannot conclude that a significant difference exists. That’s pretty straightforward, right? Below 0.05, significant.

## Is a higher T value better?

Thus, the t – statistic measures how many standard errors the coefficient is away from zero. 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.

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

If the P-value is less than (or equal to), then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than, then the null hypothesis is not rejected.

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

Suppose that you do a hypothesis test. Remember that the decision to reject the null hypothesis (H _{}) 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 _{}; if it is greater than α, you fail to reject H _{}.

## What is the critical value at the 0.05 level of significance?

The level of significance which is selected in Step 1 (e.g., α = 0.05 ) dictates the critical value. For example, in an upper tailed Z test, if α = 0.05 then the critical value is Z=1.645.

## What is the critical value at the 0.01 level of significance?

Hypothesis Test For a Population Proportion Using the Method of Rejection Regions

a = 0.01 | a = 0.05 | |
---|---|---|

Z – Critical Value for a Left Tailed Test | -2.33 | -1.645 |

Z – Critical Value for a Right Tailed Test | 2.33 | 1.645 |

Z – Critical Value for a Two Tailed Test | 2.58 | 1.96 |

## What is a positive critical value?

Think of the mean as a “mirror”. We know that the critical value at the mean is zero. Every critical value to the left of the mean is negative. Every critical value to the right of the mean is positive.

## Is P value 0.1 Significant?

Significance Levels. The significance level for a given hypothesis test is a value for which a P – value less than or equal to is considered statistically significant. Typical values for are 0.1, 0.05, and 0.01.

## Why reject null hypothesis when p value is small?

A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value. A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis.

## What do p values tell us?

The p – value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. The p – value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true.