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BigPappa
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In any significance test, if the result is greater than the cutoff it is not significant, if the result is less than the cutoff it is significant. What if it is exactly equal to the cutoff (.05) is it significant or not?
BigPappa said:In any significance test, if the result is greater than the cutoff it is not significant, if the result is less than the cutoff it is significant. What if it is exactly equal to the cutoff (.05) is it significant or not?
The significance test cutoff of .05, also known as the alpha level, is a commonly used threshold in statistical hypothesis testing. It represents the probability of obtaining a result at least as extreme as the one observed, assuming that the null hypothesis is true. In other words, if the p-value is equal to or less than .05, it is considered statistically significant and the null hypothesis can be rejected.
.05 is considered a standard and widely accepted cutoff for statistical significance. It provides a balance between minimizing the likelihood of a Type I error (rejecting the null hypothesis when it is actually true) and maximizing the power of the test (detecting a true effect when it exists). However, the choice of significance level ultimately depends on the specific research question and context.
If a result is statistically significant at the .05 level, it means that the probability of obtaining the observed result by chance alone is less than or equal to .05. This suggests that there is a low likelihood that the observed effect is due to random variation and provides evidence in support of the alternative hypothesis.
Yes, a result can be significant at a level other than .05. The significance level can be adjusted to be more or less strict, depending on the research question and the amount of acceptable risk for Type I and Type II errors. For example, a more stringent significance level of .01 may be used for studies with high stakes or when the sample size is small.
When interpreting a result that is significant at the .05 level, it is important to consider the context of the study, the strength of the effect, and the potential for other factors to influence the results. It is also important to keep in mind that statistical significance does not necessarily equate to practical or meaningful significance. Further analysis and replication of the study may be needed to fully understand the implications of a significant result.