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art: fun



Circos at British Library Beautiful Science exhibit—Feb 20–May 26


art + science

Bloomberg Businessweek Design Conference — San Francisco, 2013

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Design loves science and science loves design, but doesn't always know it. (Bloomberg Businessweek)

science design

Together with Alberto Cairo, I presented at the Bloomberg Businessweek Design Conference (highlights) on the topic of design and communication in the sciences.

Alberto, as the journalist, motivated why communication should include access to detail through an engaging narrative. He made the distinction between the specialist (heavy on detail) and the communicator (focus on narrative) and emphasized that the distinction is artificial, though often played out (watch video).

I, as the scientist, underscored the importance of clear communication between scientists. As the specialists, they are often very poor communicators. Pick up any science journal and you'll quickly discover that scientists either aren't good at telling stories or are discouraged to do so by the medium. The consequence is the same: papers read like a wall of text. TL;DR anyone? The quality of visual communication in general ranges from muddled to abysmal (watch video).

We need more leaders in the field, such as Nature Publishing Group, to reward and emphasize good visual communication (e.g. Nature Cancer Review 2013 Figure Calendar).

Our presentations concluded with a 15 minute moderated discussion with Sam Grobart, senior Businesssweek writer. Everyone got a little cheeky. Good fun.

presentation video

Watch: my presentation, conversation with Alberto Cairo, moderated by Sam Grobart. (Bloomberg TV), Albert Cairo's presentation.

presentation slides

This was a lightning 7 minute talk. I did more planning about what to say than I usually do, given that there was virtually no opportunity for any kind of backtracking, and include a running narrative below each slide.

Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
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download presentation

My slides are available as PDF, keynote (zipped) or Quicktime. The format is 16:9 HD.

Bloomberg Businessweek Design Issue

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The reality of redesign is disruptive. How can we pursue new ideas and opportunities without leaving consumers confused or angry? Businessweek puts that question to some of the world's most accomplished designers. (Bloomberg Businessweek Design Issue)

On 28 Jan 2013, Bloomberg Businessweek Design Issue will capture the ideas from the conference and the personalities that generated them.

During the conference, each talk was captured in a series of sketches by Tom Wujec: my talk sketch and moderated discussion sketch.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Date completed: ongoing — an accurate assessment of the state of the visual communication field in science. (read article)

news + thoughts

Happy Pi Approximation Day— π, roughly speaking 10,000 times

Wed 23-07-2014

Celebrate Pi Approximation Day (July 22nd) with the art arm waving. This year I take the first 10,000 most accurate approximations (m/n, m=1..10,000) and look at their accuracy.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Accuracy of the first 10,000 m/n approximations of Pi. (details)

I turned to the spiral again after applying it to stack stacked ring plots of frequency distributions in Pi for the 2014 Pi Day.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Frequency distribution of digits of Pi in groups of 4 up to digit 4,988. (details)

Analysis of Variance (ANOVA) and Blocking—Accounting for Variability in Multi-factor Experiments

Mon 07-07-2014

Our 10th Points of Significance column! Continuing with our previous discussion about comparative experiments, we introduce ANOVA and blocking. Although this column appears to introduce two new concepts (ANOVA and blocking), you've seen both before, though under a different guise.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Analysis of variance (ANOVA) and blocking. (read)

If you know the t-test you've already applied analysis of variance (ANOVA), though you probably didn't realize it. In ANOVA we ask whether the variation within our samples is compatible with the variation between our samples (sample means). If the samples don't all have the same mean then we expect the latter to be larger. The ANOVA test statistic (F) assigns significance to the ratio of these two quantities. When we only have two-samples and apply the t-test, t2 = F.

ANOVA naturally incorporates and partitions sources of variation—the effects of variables on the system are determined based on the amount of variation they contribute to the total variation in the data. If this contribution is large, we say that the variation can be "explained" by the variable and infer an effect.

We discuss how data collection can be organized using a randomized complete block design to account for sources of uncertainty in the experiment. This process is called blocking because we are blocking the variation from a known source of uncertainty from interfering with our measurements. You've already seen blocking in the paired t-test example, in which the subject (or experimental unit) was the block.

We've worked hard to bring you 20 pages of statistics primers (though it feels more like 200!). The column is taking a month off in August, as we shrink our error bars.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Analysis of Variance (ANOVA) and Blocking Nature Methods 11:699-700.

Background reading

Krzywinski, M. & Altman, N. (2014) Points of Significance: Designing Comparative Experiments Nature Methods 11:597-598.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I — t-tests Nature Methods 11:215-216.

Krzywinski, M. & Altman, N. (2013) Points of Significance: Significance, P values and t-tests Nature Methods 10:1041-1042.

...more about the Points of Significance column

Designing Experiments—Coping with Biological and Experimental Variation

Thu 29-05-2014

This month, Points of Significance begins a series of articles about experimental design. We start by returning to the two-sample and paired t-tests for a discussion of biological and experimental variability.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Designing Comparative Experiments. (read)

We introduce the concept of blocking using the paired t-test as an example and show how biological and experimental variability can be related using the correlation coefficient, ρ, and how its value imapacts the relative performance of the paired and two-sample t-tests.

We also emphasize that when reporting data analyzed with the paired t-test, differences in sample means (and their associated 95% CI error bars) should be shown—not the original samples—because the correlation in the samples (and its benefits) cannot be gleaned directly from the sample data.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Designing Comparative Experiments Nature Methods 11:597-598.

Background reading

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I — t-tests Nature Methods 11:215-216.

Krzywinski, M. & Altman, N. (2013) Points of Significance: Significance, P values and t-tests Nature Methods 10:1041-1042.

Have skew, will test

Wed 28-05-2014

Our May Points of Significance Nature Methods column jumps straight into dealing with skewed data with Non Parametric Tests.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Non Parametric Testing. (read)

We introduce non-parametric tests and simulate data scenarios to compare their performance to the t-test. You might be surprised—the t-test is extraordinarily robust to distribution shape, as we've discussed before. When data is highly skewed, non-parametric tests perform better and with higher power. However, if sample sizes are small they are limited to a small number of possible P values, of which none may be less than 0.05!

Krzywinski, M. & Altman, N. (2014) Points of Significance: Non Parametric Testing Nature Methods 11:467-468.

Background reading

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I — t-tests Nature Methods 11:215-216.

Krzywinski, M. & Altman, N. (2013) Points of Significance: Significance, P values and t-tests Nature Methods 10:1041-1042.

Mind your p's and q's

Sat 29-03-2014

In the April Points of Significance Nature Methods column, we continue our and consider what happens when we run a large number of tests.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Comparing Samples — Part II — Multiple Testing. (read)

Observing statistically rare test outcomes is expected if we run enough tests. These are statistically, not biologically, significant. For example, if we run N tests, the smallest P value that we have a 50% chance of observing is 1–exp(–ln2/N). For N = 10k this P value is Pk=10kln2 (e.g. for 104=10,000 tests, P4=6.9×10–5).

We discuss common correction schemes such as Bonferroni, Holm, Benjamini & Hochberg and Storey's q and show how they impact the false positive rate (FPR), false discovery rate (FDR) and power of a batch of tests.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part II — Multiple Testing Nature Methods 11:215-216.

Krzywinski, M. & Altman, N. (2014) Points of Significance: Comparing Samples — Part I — t-tests Nature Methods 11:215-216.

Krzywinski, M. & Altman, N. (2013) Points of Significance: Significance, P values and t-tests Nature Methods 10:1041-1042.