I’m starting to get into science a lot more these days but I do not know what p values and null hypothesis are.
Appreciate the help. Thank you.
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5 year old explanation first.:
Null hpothesis: The thing we are testing does not cause a big difference
P Value : this is the chance that the results were just from chance and not actually because of a difference.
Confidence interval: This is how sure i want to be that it wasnt just by chance we got these results
Effect size: How big was the effect of the thing we are testing.
Adult explanation.:
Null hypothesis- “There is no significant difference between what is being tested” it basically means exactly this phrase. We reject it if we find there is a big difference
.P Value- a scale of 0-1 or 0% to 100% It tells use the chance that if there really is no big difference between the data . Then there is (p in this case is .05) 5% chance that youl get results this different. ( cannot determine whether a hypothesis is true or whether results are important. )
Confidence interval- Normally its 95% aka that if you are rejecting the null hypothesis theres a 5% chances your results are a mistake.
YOU SHOULD NOT ONLY USE THE P VALUE! It does not tell you the amount of effect something had, and can basically only tell you how likely it is you probably arent just getting your results by random chance. It is considered inappropriate and statisticians have had to plead with researchers to stop doing this. [https://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503](https://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503)
1. **Example**: I want to know if loud noises makes people perform worse on a math test. So NULL HYPOTHESIS – Loud noises do not cause worse math test results.
2. Now i set a confidence interval- How likely the results of my math are giving me a reasonably correct assessment. the standard is 95%
3. So now we did the tests and have all the data. We now do what’s called a hypothesis test. And wow there are alot of different tests for theses that are complicated.
4. So we get a p value- and we hope its less than .05 if it is that means there is a significant difference from loud noises and no loud noises.
5. But value doesn’t tell us how big the difference is just that its significant. So a common one ive used to measure effect size is COHENS D The effect sizes rang from .2 small .5medium .8 large 1.3 very large.
6. If my effect size is .2 yeah i can say LOOK GUYS AUDIO REALLY EFFECTS TEST RESULTS!! but in reality its not by a whole lot.
THINGS TO LOOK OUT FOR: Often times researchers will pull intellectual tricks to hide the fact their results arent as significant as they wanted. Here are some things they will do.
* Lower the confidence interval to something much lower like .85%
* Use some slippery wording when the P is greater than .05 like Provisionally significant or bordering significant.
* Not emphasize the effect size, not show the effect size, ect.
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