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I'm not really sure what the F-ratio is, but we just finished null and alternative ratios in our AP stats class. In our class, we calculuated a p-value, and if that value is smaller than the alpha-value (significance level) you have enough evidence to reject the null hypothesis. Sorry if this doesn't help.
What else can I help you with?
What is Hypothesis Testing of Alpha Value?
Probability of rejecting a true null hypothesis; that is, the alpha value or risk you are willing to take probabilistically speaking.
Why are the larger t-ratios more likely to be statistically significant?
Larger t-ratios indicate a greater difference between the sample mean and the null hypothesis mean relative to the variability in the data. This suggests that the observed effect is less likely to be due to random chance. As a result, larger t-ratios are more likely to exceed the critical value for significance, leading to a higher probability of rejecting the null hypothesis. Thus, they often indicate stronger evidence against the null hypothesis.
When do you accept a hypothesis?
You accept an alternative hypothesis when the p-value is greater than the sample a for a confidence level of 95%. The null hypothesis cannot be accepted.
What does a p value of 0.66 tell us?
A p-value is the probability of obtaining a test statistic as extreme or more extreme than the one actually obtained if the null hypothesis were true. If this p-value is less than the level of significance (usually set by the experimenter as .05 or .01), we reject the null hypothesis. Otherwise, we retain the null hypothesis. Therefore, a p-value of 0.66 tell us not to reject the null hypothesis.
What does the critical value represent?
The critical value is used to test a null hypothesis against an alternative hypothesis at some pre-defined level of significance. A test statistic is calculated from the outcomes of a set of trials and if this test statistic is more extreme than the critical value then the null hypothesis must be rejected in favour of the alternative.
What do large values of a chi square statistic indicate?
A large value for the chi-squared statistic indicates that one should be suspiciuous of the null hypothesis, because the expected values and the observed values willdiffer by a large amount
Can you not reject null hypothesis?
you do not need to reject a null hypothesis. If you don not that means "we retain the null hypothesis." we retain the null hypothesis when the p-value is large but you have to compare the p-values with alpha levels of .01,.1, and .05 (most common alpha levels). If p-value is above alpha levels then we fail to reject the null hypothesis. retaining the null hypothesis means that we have evidence that something is going to occur (depending on the question)
What causes hypothesis to be rejected?
We have two types of hypothesis i.e., Null Hypothesis and Alternative Hypothesis. we take null hypothesis as the same statement given in the problem. Alternative hypothesis is the statement that is complementary to null hypothesis. When our calculated value is less than the tabulated value, we accept null hypothesis otherwise we reject null hypothesis.
What is the outcome of hypothesis testing?
a. the hypothesis ispartly true but needs to be revised. b. the hypothesis wrong. c. the hypothesis is supported. d. the hypothesis is of no value.
What is Hypothesis Testing of Beta Value?
Probability of failing to reject a false null hypothesis.
What is Hypothesis Testing of Alpha Value?
Probability of rejecting a true null hypothesis; that is, the alpha value or risk you are willing to take probabilistically speaking.
What is the meaning of declare value?
When forming a hypothesis for quantitative research, a declarative hypothesis states the expected relation between variables, whereas a null hypothesis states that there is no significant relation.
What is the Probabilities of Beta Value?
In hypothesis testing, this is the probability of failing to reject a false null hypothesis.
Why are the larger t-ratios more likely to be statistically significant?
Larger t-ratios indicate a greater difference between the sample mean and the null hypothesis mean relative to the variability in the data. This suggests that the observed effect is less likely to be due to random chance. As a result, larger t-ratios are more likely to exceed the critical value for significance, leading to a higher probability of rejecting the null hypothesis. Thus, they often indicate stronger evidence against the null hypothesis.
What happens when probability value is greater than alpha value?
When probability value (p-value) is greater than alpha value, we fail to reject the null hypothesis.Probablity value is the probability of obtaining an answer equal to or more extreme than the observed value.Alpha value is the level of significance. It's the value set that determines if a result is statistically significant, or in other words, if it's not likely to have occurred simply due to chance. Alpha value is usually 5%.There are two hypotheses when we conduct a hypothesis test: the null hypothesis and the alternative hypothesis.The null hypothesis acts as a default position. It's usually an assumption that there is no relationship between two events or that a treatment has no effect. In most legal systems, the null hypothesis would be that the defendant is innocent.The alternative hypothesis is what we would assume if we reject the null hypothesis. We reject the null hypothesis when the probability value is less than the alpha value.
When do you accept a hypothesis?
You accept an alternative hypothesis when the p-value is greater than the sample a for a confidence level of 95%. The null hypothesis cannot be accepted.
P-value if the null hypothesis is true?
0.5
