The alternative hypothesis states that the independent variable did affect the dependent variable, and the results are significant in terms of supporting the theory being investigated i. A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance i.
The level of statistical significance is often expressed as a p -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.
The p -value is conditional upon the null hypothesis being true, but is unrelated to the truth or falsity of the alternative hypothesis. The 6th edition of the APA style manual American Psychological Association, states the following on the topic of reporting p-values:. The only situation in which you should use a one sided P value is when a large change in an unexpected direction would have absolutely no relevance to your study.
This situation is unusual; if you are in any doubt then use a two sided P value. The term significance level alpha is used to refer to a pre-chosen probability and the term "P value" is used to indicate a probability that you calculate after a given study. The alternative hypothesis H 1 is the opposite of the null hypothesis; in plain language terms this is usually the hypothesis you set out to investigate. For example, question is "is there a significant not due to chance difference in blood pressures between groups A and B if we give group A the test drug and group B a sugar pill?
If your P value is less than the chosen significance level then you reject the null hypothesis i. It does NOT imply a "meaningful" or "important" difference; that is for you to decide when considering the real-world relevance of your result.
The choice of significance level at which you reject H 0 is arbitrary. Over 0. In the example above, the result is clear: a p-value of 0. But what if your p-value is really, really close to 0.
It's still not statistically significant, and data analysts should not try to pretend otherwise. How about saying this? Which brings me back to the blog post I referenced at the beginning. Do give it a read, but the bottom line is that the author cataloged different ways that contributors to scientific journals have used language to obscure their results or lack thereof.
As a student of language, I confess I find the list fascinating It's not right : These contributors are educated people who certainly understand A what a p-value higher than 0. Or, to put it in words that are less soft, it's a damned lie. Here are just a few of my favorites of the different ways people have reported results that were not significant, accompanied by the p-values to which these creative interpretations applied:.
I'm not sure what "quasi-significant" is even supposed to mean, but it sounds quasi-important, as long as you don't think about it too hard. But there's still no getting around the fact that a p-value of 0.
Wayne W. Module 7 - Comparing Continuous Outcomes. P-Values What to Report. P-Values A test statistic enables us to determine a p-value, which is the probability ranging from 0 to 1 of observing sample data as extreme different or more extreme if the null hypothesis were true. Nature, March 7,] Consider two studies evaluating the same hypothesis. Cautions Regarding Interpretation of P-Values There is an unfortunate tendency for p-values to devolve into a conclusion of "significant" or "not significant" based on the p-value.
If an effect is small and clinically unimportant, the p-value can be "significant" if the sample size is large. Therefore, p-values cannot determine clinical significance or relevance.
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