What do statistically significant findings imply?
In principle, a statistically significant result (usually a difference) is a result that’s not attributed to chance. More technically, it means that if the Null Hypothesis is true (which means there really is no difference), there’s a low probability of getting a result that large or larger.
What does statistical significance mean in research?
In research, statistical significance is a measure of the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer.
What does it mean if your test result is statistically significant quizlet?
Statistical significance means that the result observed in a sample is unusual when the null hypothesis is assumed to be true.
What does it mean if a variable is statistically significant?
Statistical significance is a determination by an analyst that the results in the data are not explainable by chance alone. A p-value of 5% or lower is often considered to be statistically significant.
How do you know if a chi-square is significant?
You could take your calculated chi-square value and compare it to a critical value from a chi-square table. If the chi-square value is more than the critical value, then there is a significant difference. You could also use a p-value.
What is a significant chi-square value?
The likelihood chi-square statistic is 11.816 and the p-value = 0.019. Therefore, at a significance level of 0.05, you can conclude that the association between the variables is statistically significant.
How do you accept or reject the null hypothesis in Chi-Square?
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.
What does it mean to reject the null hypothesis?
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. When this happens, the result is said to be statistically significant .
How do you know when to reject the null hypothesis 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.
How do you know if there is sufficient evidence in stats?
If the p-value is less than α, we reject the null hypothesis. If the probability is too small (less than the level of significance), then we believe we have enough statistical evidence to reject the null hypothesis and support the alternative claim.
What conclusion can you derive if you reject the null hypothesis?
Because we fail to reject the null hypothesis, we conclude that there is not sufficient evidence to support a conclusion that the population mean is greater than 166.3 lb, as in the National Transportation and Safety Board’s recommendation.
How do you use the P-value to reject the null hypothesis?
Set the significance level, , the probability of making a Type I error to be small — 0.01, 0.05, or 0.10. Compare the P-value to . If the P-value is less than (or equal to) , reject the null hypothesis in favor of the alternative hypothesis. If the P-value is greater than , do not reject the null hypothesis.
What does p value less than 0.05 mean?
P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected.
What does P value of 0.0001 mean?
Also very low p-values like p<0.0001 will be rarely encountered, because it would mean that the trial was overpowered and should have had a smaller sample size. It would seem appropriate, therefore, to require investigators to explain such results and to consider rejecting the research involved.
Is P value of 0.01 Significant?
Significance Levels. The significance level for a given hypothesis test is a value for which a P-value less than or equal to is considered statistically significant. Typical values for are 0.1, 0.05, and 0.01. These values correspond to the probability of observing such an extreme value by chance.
What does P value of 0.2 mean?
If p-value = 0.2, there is a 20% chance that the null hypothesis is correct?. P-value = 0.02 means that the probability of a type I error is 2%. P-value is a statistical index and has its own strengths and weaknesses, which should be considered to avoid its misuse and misinterpretation(12).
Why is my p value so high?
High p-values indicate that your evidence is not strong enough to suggest an effect exists in the population. An effect might exist but it’s possible that the effect size is too small, the sample size is too small, or there is too much variability for the hypothesis test to detect it.
What is the maximum P value?
A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. p-values very close to the cutoff (0.05) are considered to be marginal (could go either way). Always report the p-value so your readers can draw their own conclusions.