Mailund on the Internet

Andrew Gelman:

By “doing data analysis in a patternless way,” I meant statistical methods such as least squares, maximum likelihood, etc., that estimate parameters independently without recognizing the constraints and relationships between them. If you estimate each study on its own, without reference to all the other work being done in the same field, then you’re depriving yourself of a lot of information and inviting noisy estimates and, in particular, overestimates of small effects.

Couldn’t agree more.

See also: ESP paper rekindles discussion about statistics

Not directly related, but quite relevant.

If you are interested in statistics, I really recommend Andrew Gelman’s books and blog.

For no apparent good reason, I read an old post on p-values and re-read this comment:

John Larkin Says: 

Hi.sorry. I have trouble with the “if you repeat experiment lots of times…p value…uniformly distributed between 0 and 1″.
Is that true? If you do it lots of times do you get as many grouped around 0.0-0.01 as around 0.49-0.50?
It may be because I’m thinking of “experiments” (e.g. height of groups)…vs some statistical scenario whish uses the word stochastic – which clearly puts me in trouble.
I don’t think of pvalue as direct measure of likelihood of nul hypothesis. But if you compared two big samples (huge!) from two big groups twice (say, of height) and each experiment gave you a p-value of 0.99….I just get the feeling that these two groups might be very similar/same population…..
Cheers
JL

My answer was this

Thomas Mailund Says: 

John: Yes, p-values are uniformly distributed (under the null distribution) so you do expect to observe as many in the interval 0.0-0.01 as in 0.49-0.5.

You cannot consider a p-value of 0.99 as any kind of measure of similarity. It just doesn’t work that way.

The reason we are interested in low p-values is because if we sample from a mixture of the null distribution and the alternative distribution, then we expect more of the alternatives in the low end of p-values than we expect from the null.

Hope that helps.

Now that I think about it, this isn’t strictly true.

I still hold that p-values are uniformly distributed under the null model. So under the null model, you cannot conclude that a high p-value indicates strong support for the null model whereas low p-values support the alternative model. It doesn’t work like that.

But of course, the null model can be wrong in more than one way, and not all will show up as low p-values.

If your null model tells you that there should be a certain variance, and you see less, then you will probably see an excess of high p-values. The observations are more similar than they should be (under the null model).

You won’t see the problem as too many low p-values, but as too many high values.

If the p-values are not uniformly distributed, your null model is wrong. It can be wrong in so many ways that it really doesn’t matter why it is wrong. It is just wrong.

Hope that makes sense.

For the last year, I’ve used Mendeley as my main references database.

After I got an iPad, though, I’ve dropped it.

I really love to be able to read papers on the iPad.  I don’t have a printer at home, and I hate reading papers on the desktop, but reading and annotating papers on the iPad is great.

Just not with Mendeley. I cannot annotate papers there, and the reader isn’t really that good.

So I bought Papers for the iPad (and the desktop to be able to synchronize).

It is a really wonderful piece of software for this. I just wish that the annotations could be synchronized with the desktop as well. They are great on the iPad, but I want them on my desktop as well (damn it!).

I miss the cloud aspects of Mendeley now as well. I have to synchronize on my home computer (’cause Papers won’t let me synchronize with any old computer I find nor over the internet in general), and there is no sharing of papers and annotations as in Mendeley.

I would love to go back to Mendeley, but the iPad version is a turn off for me. As soon as the iPad version improves, though, I’m likely to go back.

Sorry for linking to a lot of Danish sites, but this story is also kind of cool: Dansk forskning skal redde chimpansen.

Its about genetics of chimps, and a project that I am involved in. We have sequenced the exomes of 30 chimps (eastern, central and western chimps) and we hope to submit out first paper in a few weeks.

It’s a really cool project, but unfortunately I cannot say more about it until it is out…

Not only did she commit scientific fraud, she even lied about her mother’s and sister’s death in a car accident (and later had them on the guest list for an award ceremony).

They were dead but got better? Some amazing doctors, I assume?

The mind boggles about how she could think that she would get away with this…

Update: More here, here and here.

In English:

• PhD student fears reputation in Penkowa case
• Documentation: Letter from 58 scientists in Milena Penkowa case
• Chairman rejects additional Penkowa inverstigation

Something is rotten in the Denmark…