You don't actually know how science works do you?
at any rate just so you're up to speed I have covered it a a little here:
the logic of statistics is you can never prove anything all you can do is disprove something!
if I want to prove a drug works, I can't, but what I can do is disprove that the drug doesn't work! Ahhhhhh the (double negative) that is the way to get to where you wanted!
we have a double blind randomized design to prove that a new wonder drug works, one half of the participants gets the wonder drug, the other half gets placebo
follow both groups and see when they will have relief from their symptoms,
1- the research design isn't flawed
now let's discuss the null hypothesis ..the 'null hypothesis is the opposite of what you want to find. group A fails to get over the sx. faster than group B.
with the null hypothesis we state it and then leave it alone .. we pass out pills and collect data next .. take the data feed it into the computer ..
what comes out T as in a t test, x^2, f etc etc.
we should only focus on the P value is key for making statistical decisions
we make decisions by putting a standard in place and comparing empirical evidence to it .. so that is what the P value is, the standard and the summary of the data .
this alpha criterion is something you decide before you make your research, you can set the value high or low, it is your discretion
people put the value of P at less than or equal to .05
a confidence interval of 95% means one is correct 95% of the time .. the other five percent is the time when one is wrong
a confidence interval of 95% corresponds exactly to a P value of </=0.05
95% chance of being right, 5 % chance of being wrong
outcomes for our study p=.02
.02 is under the bar which is very good because it means we get to 'reject the null hypothesis' because the null hypothesis is the opposite of what we are looking for .. if we reject the null hypothesis, the drug works!
is it possible that the drug works in the study but not out in the world?
yes possible though unlikely..
what this means is that we have made a type one error, or alpha error, this type of error basically states we rejected the null hypothesis but we shouldn't have . You'll never know for sure if you have made a type one error, all you know is the chance that you made a type one error that chance is found in the p value a .02 i.e a 2% chance .
p value is type one error
if the number for the p value gets too low, we'll take that chance
2nd outcome for the study, p=1.3 we are now above the bar, we can't reject the null hypothesis, we fail to reject the null hypothesis..
you never 'accept the null hypothesis'
same as 'jury logic' not that you are innocent, just that there isn't enough evidence to convict you . the chance for a type one error here? = 0 why? because to make a type one error you must first reject the null hypothesis.
however in this case we could have made a beta type error means, I didn't reject the null hypothesis but I should have .. in other words in the study the drug is crap, but out in the real world, it works well..
chance of making a type two error? we don't know.. can't look at P value because P value only tells us a type one error ONLY!
type one error is considered worse ..
which is worse looking at you and lying or simply forgetting to tell you something?
lying is worse, that is a type one error a 'sin of commission' because first do no harm is a physician's oath.
now you are giving this new drug because it has been approved and works great, the patient is now asking this drug works great in research, what is the chance it will work for me? best response is 'I don't know' this gives you statistical significance not clinical significance!
you can answer the patient by looking at the table the one that tells you, who got the drug, who didn't get the drug, got better, didn't get better
got drug got better 70%
got drug didn't get better 30%
no drug better 30%
not better no drug 70%
pt.s chance of getting better on drug
the answer here is 70% chance of getting better out of one hundred people that got the drug 70% of them got better!
best,