How does the t-value relate to the p-value?
The larger the absolute value of the t-value, the smaller the p-value, and the greater the evidence against the null hypothesis.
What does 5% significance level mean?
The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
What is the difference between P test and t-test?
The main difference between T-test and P-Value is that a T-Test is used to analyze the rate of difference between the means of the samples, while p-value is performed to gain proof that can be used to negate the indifference between the averages of two samples.
What is a 10 significance level?
The significance level usually is chosen in consideration of other factors that affect and are affected by it, like sample size, estimated size of the effect being tested, and consequences of making a mistake. Common significance levels are 0.10 (1 chance in 10), 0.05 (1 chance in 20), and 0.01 (1 chance in 100).
What is a 1% significance level?
The significance level is the Type I error rate. So, a lower significance level (e.g., 1%) has, by definition, a lower Type I error rate. And, yes, it is possible to reject at one level, say 5%, and not reject at a lower level (1%). I show an example of this in my post about p-values and significance levels.
What does p-value of 0.03 mean?
3%
The p-value 0.03 means that there’s 3% (probability in percentage) that the result is due to chance — which is not true.
Do you want p-value to be high or low?
The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).
What is a good t-value?
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.
How do you interpret t values?
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.
