exactly about the issue of using your data to come up with your hypotheses.
They work in the area of Multiple Sclerosis.
it's sort of getting a greater amount of blood in the area.
the hypotheses and interrogated the hypotheses with the same data set.
their emphasis was on doing this in a rigorous fashion.
which is there to detect brain activation.
And of course there's no brain activation in a dead salmon.
you don't account for that correctly, you can see brain activation in a dead salmon.
at least in the past decade, was from the Women's Health Initiative.
knowledge for the efficacy of hormone replacement therapy and the safety of it.
of randomized trial versus the value of a lot of observational data.
for the purposes of this lecture.
not women were on hormone replacement therapy and watched them over time.
where women were actually randomized to hormone replacement therapy or not.
asked questions relating to some of the studies that have done since then.
refine the science on this particular issue.
randomization was In this particular setting.
where you can't actually measure what you'd like to.
So in two ways in this study, you can't measure what you'd like to.
Susan Bassett, was interested in studying Alzheimer's disease.
with regard to these functional magnetic resonance imaging scans?
maybe you could treat it.
is trying to find precursors for the disease.
First of all, the fMRI machine doesn't actually measure brain activation.
It measures a proxy for brain activation through blood oxygenation.
extent that we'd like, the brain activation in the way that we'd like.
it only measures a proxy.
then talk about another way in which we're not measuring what we like.
were at very high familial risks.
people with no affected siblings and affected parents.
is gonna contract Alzheimer's disease.
Quite the opposite, relatively small fraction of them has.
disease, and some of them did, eventually.
This was a long study, over 15 years.
So later on they saw some results for people who actually converted.
But the point being here that she can't actually measure what she needs to.
Because if you did, the problem wouldn't be worth studying, right?
That's everything that you'd like to do.
what you have to combat this with is giant sample sizes.
Alzheimer's disease in the one group and a smaller percentage in the other group.
the fewer people of control type in the other group.
like to have, isn't the measurement that you get.
you need to work around it.
experiments that anyone had seen at the time, just to combat this issue.
as our launching point for all of our discussions.
the people doing the data analysis if it's not you and you're the manager.
this collection of various problems that arise in data analysis.
So welcome to the class, and we look forward to seeing you in it.