Ethan White

Nathan over at [Flowing Data](http://flowingdata.com){target="_blank"}
just posted [an interesting
piece](http://flowingdata.com/2009/06/04/rise-of-the-data-scientist/) on
the emergence of a new class of scientists whose work focuses on the
manipulation, analysis and presentation of data. The take home message
is that in order to fully master the ability to understand and
communicate patterns in large quantities of data that one needs to have
some ability in:

- Computer science -- for acquiring, managing and manipulating data
- Mathematics and Statistics -- for mining and analyzing data
- Graphic design and Interactive interface design -- to present the
  results of analyses in an easy to understand manner and encourage
  interaction and additional analysis by less technical users

His point is that while one could get together a group of people (one
with each of these skills) to undertake this kind of task, that the
challenges of cross-disciplinary collaboration can slow down progress
(or even prevent it entirely). As such, there is a need for individuals
that have at least some experience in several of these fields to help
facilitate the process. I think this is a good model for this kind of
work in ecology, though given the already extensive multidisciplinarity
required in the field I view this role as one occupied only be fairly
small fraction of folks.

The other thing that I really liked about this post (and about Flowing
Data's broader message) is the focus on the end user. The goal is to
make ideas and tools available to the broadest possible audience and
[sometimes]{style="text-decoration:line-through;"} often the more
technical folks in the biological scientists seem to forget that their
goal should be to make things easy to understand and simple for
non-technical users to use. This is undoubtedly a challenging task, but
one that we should work to accomplish whenever possible.


Nathan over at Flowing Data just posted an interesting piece on the emergence of a new class of scientists whose work focuses on the manipulation, analysis and presentation of data.

Data scientists

You can always count on Andrew Gelman for [quality April Fools Day
posts](http://www.stat.columbia.edu/~cook/movabletype/archives/2009/04/why_tables_are.html).


You can always count on Andrew Gelman for quality April Fools Day posts.

April fools for the statistically inclined

> Data is the sword of the 21st century, those who wield it well, the
> Samurai.
>
> -- Jonathan Rosenberg, SVP, Product Management, Google  


Ecological Samuri

::: {.asset-content .entry-content}
::: {.asset-body}
Andrew Gelman over at [Statistical Modeling, Causal Inference, and
Social Science](http://www.stat.columbia.edu/%7Ecook/movabletype/mlm/)
posted a [hilariously awful
story](http://www.stat.columbia.edu/~cook/movabletype/archives/2009/02/not-statistical.html)
about the interpretation of a non-significant result he saw at a recent
talk (I particularly love the Grrrrrrr at the end).

> I'm always yammering on about the difference between significant and
> non-significant, etc. But the other day I heard a talk where somebody
> made an even more basic error: He showed a pattern that was not
> statistically significantly different from zero and he said it was
> zero. I raised my hand and said something like: It's not \_really\_
> zero, right? The data you show are consistent with zero but they're
> consistent with all sorts of other patterns too. He replied, no, it
> really is zero: look at the confidence interval.
>
> Grrrrrrr.

This and related misinterpretations crop up all the time in ecology.
I've witnessed some particularly problematic cases where the scientist
is interested in attempting to determine if some data are consistent
with a theoretically predicted parameter and the confidence intervals
are relatively wide. The CIs sometimes contain both 0 and the
theoretically predicted value and yet it is concluded that the data are
not consistent with the model because the parameter is "not
significant". This is obviously problematic given that the goal of the
analysis in the first place had nothing to do with demonstrating a
difference from 0.
:::
:::


Andrew Gelman over at Statistical Modeling, Causal Inference, and Social Science posted a hilariously awful story about the interpretation of a non-significant result he saw at a recent talk (I particularly love the Grrrrrrr at the end).  This and related misinterpretations crop up all the time in ecology.

Messaggi di Rogue Scholar

Data scientists

April fools for the statistically inclined

Ecological Samuri

Definitely not the meaning of “non-significant”