Saturday, May 03, 2014

Some quotes about statistics or from statisticians

I often see people cite interesting quotes in their presentation. Citing a good quote can entice the audience. I recently saw a presentation with the following quote about the statistics in dealing with uncertainties:
One way of defining statistics is…
The science of quantifying uncertainty, dealing with uncertainty, and making decisions in the face of uncertainty…
…and drug development is a series of decisions under huge uncertainty.
There is a website that lists the famous quotes from Statisticians or about Statisticians

Here are some quotes with top ranking:  

All models are wrong, but some are useful. (George E. P. Box)

An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem. (John Tukey)

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. (Ronald Fisher (1938))

Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital. (Aaron Levenstein)

Statisticians, like artists, have the bad habit of falling in love with their models. (George Box)

I think it is much more interesting to live with uncertainty than to live with answers that might be wrong. (Richard Feynman)

If you torture the data enough, nature will always confess. (Ronald Coase)

Absence of evidence is not evidence of absence. (Carl Sagan)

It's easy to lie with statistics; it is easier to lie without them. (Frederick Mosteller)
Then there is a following cartoon::

Thursday, May 01, 2014

New books about 'missing data in clinical trials'

Missing data is one of the critical issues in statistics and in clinical trials. Three new books about the missing data were written by the biostatisticians working directly in pharmaceutical and drug development fields. These three books are worth recommending.

    For free books regarding the missing data, the following resources are available: 
    Does FDA or EMA have a preferred method for dealing with missing data for when companies submit new drugs for approval?
    The general trends in handling the missing data: