Oh great, so you need to be a Jew in order to make a formal denial? This atheist thing is starting to be more troublesome than I imagined.
Kosher is also commonly defined as "legitimate". "she consulted lawyers to make sure everything was kosher" is actually an example sentence if you type, "define: kosher" into google.
I am not sure why this is so unclear. The null hypothesis of "no treatment effect" is equivalent to saying "both groups were sampled from the same distribution" which is equivalent to "the means of the treatment group and control groups are exactly equal"
The p-value is the probability of getting as or more extreme of the difference between sample means that you observed assuming that there is in reality no difference in population means. If the treatment and control groups are different at baseline (or become different over the course of the study for reasons other than the treatment) then this assumption is false.
What you quoted doesn't say what you just said. "both groups were sampled from the same distribution" is not the same as "the means of the treatment group and control groups are exactly equal." The *expected* means of the groups is equal. The math behind the statistical models takes into account random group assignment and so there is no expectation that the groups start exactly the same (or change in similar ways over the course of the experiment).
Ronald Fisher it is: mathematics of a lady tasting tea http://books.google.dk/books?i...
See the section "The Null Hypothesis"
It is evident that the null hypothesis must be exact, that is free from vagueness and ambiguity, because it must supply the basis of the “problem of distribution,” of which the test of significance is the solution. A null hypothesis may, indeed, contain arbitrary elements, and in more complicated cases often does so: as, for example, if it should assert that the death-rates of two groups of animals are equal, without specifying what these death-rates usually are. In such cases it is evidently the equality rather than any particular values of the death-rates that the experiment is designed to test, and possibly to disprove.
And what point did you hope to make?
How do you get "treatment had no effect" into the t-test calculation? You compare the difference between means that you got with what you would expect if the difference between means was zero.
.....and your point is? And how does this relate to your initial statement that scientists "assume that the treatment group and control group were exactly the same at baseline"?
"If the treatment had no effect at all we'd expect to see results like this x% of the time just based on the randomness of group assignment."
At no point in this process did the scientists assume that the treatment group and the control group were exactly the same.....
"If mu1=mu2 we'd expect to see results like this x% of the time just based on the randomness of group assignment."
Can you see the assumption?
What you have in quotes isn't what I said. The assumptions in the part that I wrote that you quoted were that the treatment had no effect and that group assignment was random.
If you'd like to spell out the assumptions in your statement you are welcome to do that.
TC Wilcox clearly I am not an authoritative enough entity for you to listen to why you are wrong. You have also apparently failed to perform the calculations yourself and understand the implications. What person or organization would you like as a source?
Dude, I am listening to you (and wasting my time apparently) trying to explain why your claim that scientists assume that "treatment group and control group were exactly the same at baseline" is false, false, FALSE.
You tried to back way from that by asking me if "two groups would have the exact same average if there were no treatment effect". That is once again false (but in my opinion less false). They would have the exact same expected average, but I wouldn't expect them to have the same exact average.
If you would like to explain to me what I said exactly that is incorrect you are welcome to do that. So far I don't think you've actually claimed that anything I've said is incorrect.
If you'd like to go to a third party that is fine with me to. I'd accept anyone that is really good at Math or Statistics. A PhD just to show they (probably) know what they are talking about would make them more acceptable.
You want to compare with a t-test or anova. How do you test the null hypothesis that the treatment has no effect?
Do you claim that it is possible to do this without first assuming that the two groups would have the exact same average if there were no treatment effect? If so I would like to learn your new method.
You should study some statistics. You seem to some (very) incorrect ideas about what the tests mean.
This is how it works. In a randomized study scientists will assign subjects two a treatment group and a control group. As you might guess assignment to a group is done randomly.
Now we go to one of the big misconceptions you seem to keep going back to. You state that scientists assume "treatment group and control group were exactly the same at baseline." They do NOT! On purpose they are creating random groups. They are making no effort at all to make the groups equal and in fact if they made any effort to make the groups equal it would invalidate the math behind the statistics and the experiment would have no real value.
They have two groups and they are as sure as they can be that the two groups aren't equal because groups were assigned randomly. They do whatever it is they want to do and then they measure whatever it is they want to measure. Statistics tells the scientists how likely it is that any differences they see between the groups are caused by random chance. In other words, statistics might say something like, "If the treatment had no effect at all we'd expect to see results like this x% of the time just based on the randomness of group assignment."
At no point in this process did the scientists assume that the treatment group and the control group were exactly the same.....
And if you want to learn this stuff you most certainly can. Pick up a cheap textbook on statistics. Read it and try to understand it. Be careful to make sure that you are learning not just how to run a t-test, but the assumptions that are built into a statistical model.
Oh really. What null hypothesis was used? Was it that the mean of the treatment group is equal to the mean of the control group?
The null hypothesis would probably normally be that the treatment has no effect. In some studies it might be that there is no relationship between the things that are being measured. The null hypothesis does not mean, "we assume that that the two groups are exactly the same". You thinking it means that once again shows that you know very little about statistics or how statistics is used in science.
It is simple, anyone can understand it. They assume that the treatment group and control group were exactly the same at baseline and that nothing happened during the course of the study that could make the groups differ on average. Do you believe any two groups of people are exactly the same?
You haven't studied statistics, have you? You might want to try and learn a little introductory statistics.
I don't know in particular which study you are referring to, but I've never seen or heard of a medical study where the doctors assumed the control group and the treatment group are exactly the same. Assuming proper statistical protocols were followed statistics tells us what the odds are that what we observed is just a random by-product of how people were assigned to groups.
In short, you don't understand the math behind these experiments and so you are convinced that the experimenters have made silly assumptions.
I would say be careful about letting female engineers interview other potential candidates unless they are known to be genuinely fair-minded. You very well may find that it's actually the women, not the men, who are discriminating.
I would be careful about generalizing here. Some women probably discriminate against other women. Some men probably do the same thing. When you paint with too broad a brush you run the risk of fueling "sisterhood versus the patriarchy" arguments.
How silly! You're making the mistake of assuming than any Democratic administration, much less our current Chicago machine one, is against patent trolls. Look at how much money lawyers give to the Democratic party. Or check and you'll find that the East Texas judge that just ruled in favor of Lodsys was a Obama appointee.
Empirical observation - Patent trolls have done quite well no matter which party has been in party.... It is almost like neither party cares to stop them....
Robot Turtles is a board game for kids ages 3-8. It takes seconds to learn, minutes to play, and will keep them learning for hours. Kids won't know it but while they're playing, they're learning the fundamentals of programming.
"It's the 99 percent of people who give the rest a bad name."
Fixed that for you.
Yep, politics as usual... Glad to know that the richest nation on Earth could care less when 70,000 innocent people die as long as their skin is brown and/or they live in the desert.
Many, many more might die if we get involved. Millions might die. How can you be sure that the US escaling violence by bombing will result in fewer deaths long-term? Also, what if the facts being spouted on the news are incorrect? This whole thing sounds worse than invading Iraq for weapons of mass-destruction and we all know how well that ended.
Fermat's last theorem is easy to prove if one takes this conjecture as true. I have discovered a truly marvelous proof of this, which this comment box is too small to contain.
Fixed that for you.
The Tao is like a glob pattern: used but never used up. It is like the extern void: filled with infinite possibilities.
