As a rule of thumb, what t-score do researchers believe to be sufficiently significant for a T-test?
1. 1.96
2. 2
3. 2.54
4. 4
Answer (2)
In statistics, the T-test is used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-score is a critical value that helps decide whether to reject the null hypothesis.
For a T-test, the t-score's significance level can vary based on the context and the degrees of freedom, but as a rule of thumb, researchers often use the following t-scores as significant cut-off points for specific confidence levels:
1.96 - This is the common critical value for a 95% confidence level in a two-tailed test, assuming a normal distribution. It means that if the absolute value of the t-score is greater than 1.96, the result is statistically significant at the 0.05 level.
2 - This is not typically a standard critical value, but it is sometimes used as a quick estimation for significance.
2.54 - This value is associated with a lower probability, around 99% confidence level in a two-tailed test. It's a more stringent level of significance.
4 - Extremely rare and implies a very high level of significance, which is unusual for typical studies unless the difference between groups is very large or the sample size is substantial.
Given the options, the correct multiple choice for a common rule of thumb for significance in a T-test would be 1.96 for a standard level of significance at 95% confidence in a two-tailed test.
The t-score considered significantly sufficient for a T-test at a 95% confidence level is 1.96 . This means that if the absolute value of the t-score is greater than 1.96, we reject the null hypothesis. Thus, the correct answer from the options provided is 1.96 .
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