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If every experimental study uses averages then you should be able to use any experimental study and show us how.
Medical studies, please, since the thread is on a medical topic.
It is honestly too ridiculous to have to explain, but maybe it will stop you from this relentless repeated nonsense.
Here is a pretend simple experiment, designed just for you. (All published research would be way over your head and you would never be able to understand how they are analyzed).
We will imagine there is an experiment that tries to find out if the drug zyloplamodamadlug works to raise IQ scores. There are let's say 100 subjects, and they are assigned randomly to either the treatment group that gets the drug, or the placebo group that gets an inactive pill.
All subjects' IQs are tested before the experiment.
The independent variable is the drug. The dependent variable is the change in IQ score.
The duration of the experiment will be 6 months, decided ahead of time.
At the end of 6 months, all subjects' IQs are tested again, and the change in each IQ is recorded.
The average (mean) change in IQ is calculated for the treatment group, and for the drug group. It is found that the treatment group, that got the drug, had an average IQ increase of 10 points. The placebo group had an average increase of 2 points.
Looks like the drug is a winner! Wait, not so fast. First we must calculate the variance for each group. When variance is high, there is more likelihood the difference between the 2 means happened by chance, because of factors other than the drug.
So we calculate the variance, and the standard deviation, and from that we can do something called a T test, which gives us the probability that the result did not happen by chance. If that P value is below a certain cut off point, then we feel justified in rejecting the null hypothesis. That means we reject the idea that the difference in the 2 means happened by chance. We assume the difference was likely because of the drug.
Bingo, another win for Big Drug!
This is the simplest kind of experiment. And we assumed (not necessarily rightly) that the IQ distributions turned out to be normal. If they are skewed, if there are extreme outliers, we must do something about that. But let's just keep it simple.
I VERY MUCH DOUBT you understood from this simple example how averages are typically used in experimental research. But this is it, I am not giving you countless more examples until someday, maybe in the next decade, if ever, you finally get this UTTERLY SIMPLE concept.
It is honestly too ridiculous to have to explain, but maybe it will stop you from this relentless repeated nonsense.
Here is a pretend simple experiment, designed just for you. (All published research would be way over your head and you would never be able to understand how they are analyzed).
We will imagine there is an experiment that tries to find out if the drug zyloplamodamadlug works to raise IQ scores. There are let's say 100 subjects, and they are assigned randomly to either the treatment group that gets the drug, or the placebo group that gets an inactive pill.
All subjects' IQs are tested before the experiment.
The independent variable is the drug. The dependent variable is the change in IQ score.
The duration of the experiment will be 6 months, decided ahead of time.
At the end of 6 months, all subjects' IQs are tested again, and the change in each IQ is recorded.
The average (mean) change in IQ is calculated for the treatment group, and for the drug group. It is found that the treatment group, that got the drug, had an average IQ increase of 10 points. The placebo group had an average increase of 2 points.
Looks like the drug is a winner! Wait, not so fast. First we must calculate the variance for each group. When variance is high, there is more likelihood the difference between the 2 means happened by chance, because of factors other than the drug.
So we calculate the variance, and the standard deviation, and from that we can do something called a T test, which gives us the probability that the result did not happen by chance. If that P value is below a certain cut off point, then we feel justified in rejecting the null hypothesis. That means we reject the idea that the difference in the 2 means happened by chance. We assume the difference was likely because of the drug.
Bingo, another win for Big Drug!
This is the simplest kind of experiment. And we assumed (not necessarily rightly) that the IQ distributions turned out to be normal. If they are skewed, if there are extreme outliers, we must do something about that. But let's just keep it simple.
I VERY MUCH DOUBT you understood from this simple example how averages are typically used in experimental research. But this is it, I am not giving you countless more examples until someday, maybe in the next decade, if ever, you finally get this UTTERLY SIMPLE concept.
Ok, that's it. I knew you would not accept any kind of answer. Are you suggesting that a real study would somehow NOT use averages the same way as my example study?
Why don't YOU show me a published experiment that does NOT use any averages. Why don't YOU show me that comparing group means (average) is NOT a standard way to evaluate an experiment's results.
I don't know what your game is here, but I am really tired of it. Maybe somebody with knowledge could spend 5 seconds and step in and explain it to her?
And, of course, experiments do not always compare averages. If the outcome measure is death, for example, they might compare the number of deaths in each group, or the percentage of subjects who died in each group.
I added this because someone who desires to be difficult might say I am all wrong because averages are not always used. Very often, averages are used. This is NOT controversial, there is NO ONE who can make a case that this statement is wrong.
And, of course, experiments do not always compare averages. If the outcome measure is death, for example, they might compare the number of deaths in each group, or the percentage of subjects who died in each group.
I added this because someone who desires to be difficult might say I am all wrong because averages are not always used. Very often, averages are used. This is NOT controversial, there is NO ONE who can make a case that this statement is wrong.
So why the ongoing debate? Beats me. Really.
You were the one who claimed "every experimental study uses averages".
You were the one who claimed "every experimental study uses averages".
Hmmm. Let's see, what else can we nit pick on. I said EVERY? Really? And OH NO there could be exceptions.
You are not interested in having any kind of rational conversation here. Your motive, whatever it may be, is quite bizarre.
But AT LEAST, I guess, you finally have to admit you were wrong when you said averages have nothing at all to do with medical research.
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