What happens if F statistic is greater than critical value?

If the F-statistic is greater than the critical value, you reject the null hypothesis and conclude that there is a structural break in the data.
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What happens if F value is greater than critical value?

If our F -statistic is greater than the critical value then we conclude that there insufficient evidence to suggest that the two populations come from the same population of scores. Therefore, we accept the hypothesis that the sample variances are significantly different.
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What does it mean if the F value is less than the F critical value?

If the F statistic is smaller than the critical F value:

The sample averages are not significantly different from each other. The observed differences among the sample averages could reasonably be due to random chance alone. The result is not statistically significant.
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What if the test statistic is higher than the critical value?

If the test statistic is more extreme than the critical value, the null hypothesis is rejected.
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When F is equal to or greater than the critical value?

If your obtained value of F is equal to or larger than this critical F-value, then your result is significant at that level of probability.
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How to read F Distribution Table used in Analysis of Variance (ANOVA)

What does a large F statistic mean?

When the F value is large, it indicates that the differences in group means are substantially greater than what could be expected due to random variation alone. Conversely, a small F value suggests that the observed differences in means are likely attributable to chance fluctuations within the groups.
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What if the calculated value is greater than the critical value?

If your chi-square calculated value is greater than the chi-square critical value, then you reject your null hypothesis. If your chi-square calculated value is less than the chi-square critical value, then you "fail to reject" your null hypothesis.
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What does the F statistic in ANOVA measure?

In ANOVA (analysis of variance), the F value is a test statistic that measures the ratio of between-group variability to within-group variability. The F value is calculated by dividing the variance between the group means by the variance within the groups.
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What if test value is less than critical value?

If the value of the test statistic is less extreme than the critical value, then the null hypothesis cannot be rejected. However, if the test statistic is more extreme than the critical value, the null hypothesis is rejected and the alternative hypothesis is accepted.
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What if chi is greater than critical value?

If the Chi-Square value does not exceed the critical value (e.g. p=0.05 ) then the null hypothesis will be accepted i.e. the data does follow the hypothetical pattern. If it does exceed the critical value, the null hypothesis must be rejected.
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What if the F-test value is greater than 1?

If the F-score is much greater than one, the variance between is probably the source of most of the variance in the total sample, and the samples probably come from populations with different means. Keeping the calculations organized is important when you are finding the variance within.
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What happens if the computed F value exceeds the critical F value in a one way Anova?

Answer and Explanation:

If we perform a one-way analysis of variance and we come up that the computed F statistic is greater than the critical F value, then we can: b) reject the null hypothesis since there is evidence that not all the means are different.
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How to interpret an F statistic?

A large F-statistic value proves that the regression model is effective in its explanation of the variation in the dependent variable and vice versa. On the contrary, an F-statistic of 0 indicates that the independent variable does not explain the variation in the dependent variable.
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What does it mean if F value is greater than p-value?

If you get a large f value, it means something is significant, while a small p value means all your results are significant. The F statistic just compares the joint effect of all the variables together. To put it simply, reject the null hypothesis only if your alpha level is larger than your p value.
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What if the F critical value in the table is smaller than the F value calculated from the data?

If your F statistic is less than or equal to the F critical value, there isn't enough evidence to say that the group variances are significantly different from each other. You would fail to reject the null hypothesis, meaning that any observed differences could likely be due to chance.
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What if the F statistic is less than 1?

When F is less than one, we would not reject the hypothesis of no differences. So, when we look at patterns of means when F is less than 1, we should see mostly the same means, and no big differences.
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What happens if you exceed the critical value?

Critical values play a crucial role in hypothesis testing as they determine the threshold for rejecting or failing to reject the null hypothesis. When the calculated test statistic exceeds the critical value, it indicates that the observed data is significantly different from what is expected under the null hypothesis.
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What does it mean if the test statistic is greater than the critical value?

That is, it entails comparing the observed test statistic to some cutoff value, called the "critical value." If the test statistic is more extreme than the critical value, then the null hypothesis is rejected in favor of the alternative hypothesis.
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What to do if p-value is greater than critical value?

The null hypothesis is rejected if the p-value is less than or equal to the specified significance level α . Otherwise, the null hypothesis is not rejected. Note: if p≤α p ≤ α , reject H0 ; otherwise, if p>α , do not reject H0 .
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What is considered a high F statistic?

An extremely high F statistic implies that MSb>>MSw (the variance between group means is very large compared to the variance within each group). To put it simple, the values within each group do not differ significantly from one another, while those across different groups do.
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What is a good significance F value?

By rule of thumb, an F-value of greater than 4.0 is usually statistically significant but you must consult an F-table to be sure. If F is significant, than the regression equation helps us to understand the relationship between X and Y.
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How do you report an F statistic?

The key points are as follows:
  1. Set in parentheses.
  2. Uppercase for F.
  3. Lowercase for p.
  4. Italics for F and p.
  5. F-statistic rounded to three (maybe four) significant digits.
  6. F-statistic followed by a comma, then a space.
  7. Space on both sides of equal sign and both sides of less than sign.
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What if the F statistic is greater than the critical value?

The F critical value is a specific value that is used to determine whether the F statistic is statistically significant. In general, if the F statistic is greater than the F critical value, then you can reject null hypothesis; this means that there is a significant difference between the group variances.
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What if the critical value is less than the test statistic?

If the statistic is less than or equal to the critical value, we fail to reject the null hypothesis (e.g. no effect). Otherwise it is rejected. We can summarize this interpretation as follows: Test Statistic <= Critical Value: Fail to reject the null hypothesis of the statistical test.
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What decision should be made if the computed value is greater than the critical value?

Reject the null hypothesis if the sample's t value is greater than the critical value of t. Otherwise, don't reject the null hypothesis.
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