Increasing the alpha level increases your chance of rejecting the null, but it also increases the chance of Type I error. You just studied 10 terms!
How can a null hypothesis be rejected?
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. ... If the P-value is less than (or equal to) , reject the null hypothesis in favor of the alternative hypothesis.
Which factor will increase the chances of rejecting the null hypothesis?
When we increase the sample size , decrease the standard error, or increase the difference between the sample statistic
What makes it easier to reject the null?
Higher values of α make it easier to reject the null hypothesis, so choosing higher values for α can reduce the probability of a Type II error.
What causes a hypothesis to be rejected?
05. If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true , then the null hypothesis is rejected. ... If there is greater than a 5% chance of a result as extreme as the sample result when the null hypothesis is true, then the null hypothesis is retained.
Under what circumstances can a very small treatment effect be statistically significant?
Under what circumstances can a very small treatment effect be statistically significant? If the sample size is small and the sample variance is large. If the sample size is big and the sample variance is small. If the sample size and the sample variance are both small.
What is the consequence of a type II error quizlet?
In typical research situation, a type II error means that the hypothesis test has failed to detect a real treatment effect . The concern is that the research data does not show the result the researcher hoped to obtain.
How do you know when to reject the null hypothesis?
- 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.
Why do we reject the null hypothesis if/p α?
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). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.
When you reject the null hypothesis is there sufficient evidence?
Option 1) Reject the null hypothesis (H0). This means that you have enough statistical evidence to support the alternative claim (H1).
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.
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.
What is the outcome when you reject the null hypothesis when it is false?
When the null hypothesis is false and you fail to reject it, you make a type II error . The probability of making a type II error is β, which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power.
How do you solve the null and alternative hypothesis?
| H 0 H a | equal (=) not equal (≠) or greater than (>) or less than (<) | greater than or equal to (≥) less than (<) | less than or equal to (≤) more than (>) |
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Does rejecting the null hypothesis mean the alternative hypothesis is true?
In a similar way, a failure to reject the null hypothesis in a significance test does not mean that the null hypothesis is true. It only means that the scientist was unable to provide enough evidence for the alternative hypothesis . ... As a result, the scientists would have reason to reject the null hypothesis.
What is the null hypothesis for at test?
“The statement being tested in a test of statistical significance is called the null hypothesis. The test of significance is designed to assess the strength of the evidence against the null hypothesis. Usually, the null hypothesis is a statement of ‘no effect’ or ‘no difference’.” It is often symbolized as H 0 .
