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The Nature Methods Points of View column column offers practical advice in design and data presentation for the busy scientist.
With the publication of Uncertainty and the Management of Epidemics, we celebrate our 50th column! Since 2013, our Nature Methods Points of Significance has been offering crisp explanations and practical suggestions about best practices in statistical analysis and reporting. To all our readers and coauthors: thank you and see you in the next column!

Nature Methods: Points of Significance

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Points of Significance column in Nature Methods. (Launch of Points of Significance)

Generated on 13-May-2024 (9 days ago).

Metrics are provided by Altmetric.

Access values larger than 10,000 are rounded off to nearest 1,000 by Altmetric.

article accesses daily %nm %all cit (ws) cit (cross) altmetric
Significance, P values and t-tests 223,000 58 91 98 95 94 94
Error bars 208,000 54 98 99 163 178 187
Visualizing samples with box plots 198,000 53 86 98 343 367 82
Principal component analysis 193,000 77 86 96 463 763 80
Association, correlation and causation 172,000 55 97 99 157 186 183
P values and the search for significance 133,000 49 94 98 56 68 156
Replication 129,000 36 60 93 100 92 26
Statistics versus machine learning 123,000 55 97 99 202 787 351
Importance of being uncertain 117,000 30 80 96 52 56 52
Power and sample size 107,000 28 79 97 113 129 56
Nonparametric tests 64,000 17 56 88 49 54 13
Comparing samples—part I 63,000 17 68 92 25 33 21
Analysis of variance and blocking 60,000 17 62 92 41 48 19
Nested designs 59,000 17 86 97 40 38 56
Bayes' theorem 58,000 17 73 95 54 56 38
Two-factor designs 57,000 16 34 51 20 27 3
Comparing samples—part II 53,000 14 47 87 48 51 12
Sources of variation 52,000 15 57 92 15 18 17
Simple linear regression 51,000 16 76 93 63 81 29
Split plot design 51,000 15 40 78 42 46 7
The SEIRS model for infectious disease dynamics 48,000 33 98 99 72 102 358
Bayesian statistics 48,000 15 57 90 16 17 17
Designing comparative experiments 48,000 13 52 83 11 14 9
Interpreting P values 46,000 17 75 95 35 51 60
Sampling distributions and the bootstrap 46,000 14 54 91 61 75 18
Multiple linear regression 44,000 14 81 95 62 79 43
Classification and regression trees 40,000 16 46 80 122 185 10
Classification evaluation 38,000 13 70 95 171 223 45
Model selection and overfitting 36,000 13 66 92 309 358 27
Bayesian networks 35,000 11 45 77 29 26 7
Clustering 32,000 13 50 89 64 87 20
Optimal experimental design 25,000 12 50 89 43 53 21
Analyzing outliers: influential or nuisance? 25,000 8 35 68 51 56 5
Machine learning: supervised methods 22,000 9 56 92 128 181 24
Logistic regression 21,000 7 68 92 62 70 25
Regression diagnostics 21,000 7 46 72 37 47 6
Ensemble methods: bagging and random forests 18,000 7 37 79 96 135 9
Modeling infectious epidemics 17,000 11 85 97 45 64 111
The curse(s) of dimensionality 17,000 8 58 89 145 192 21
Machine learning: a primer 16,000 7 89 97 63 91 80
Tabular data 13,000 5 28 64 2 2 4
Regularization 12,000 4 54 87 28 26 14
Two-level factorial experiments 10,000 5 12 24 10 10 1
Predicting with confidence and tolerance 7,723 4 34 69 3 5 6
Markov models—Markov chains 7,249 4 40 90 12 16 23
Markov models — hidden Markov models 5,804 3 25 79 16 20 9
The standardization fallacy 4,860 4 47 93 24 25 32
Convolutional neural networks 4,752 17 17 72 0 5 6
Quantile regression 4,049 2 36 78 14 55 9
Markov models — training and evaluation of hidden Markov models 4,016 3 41 85 5 4 12
The class imbalance problem 3,665 4 20 75 17 32 7
Analyzing outliers: robust methods to the rescue 3,441 2 25 50 16 18 3
Uncertainty and the management of epidemics 3,423 3 21 70 5 9 7
Survival analysis—time-to-event data and censoring 3,322 5 17 68 2 6 4
Neural networks primer 2,957 6 19 69 1 2 4
Graphical assessment of tests and classifiers 2,152 2 28 83 4 4 11
Regression modeling of time-to-event data with censoring 1,857 3 11 71 1 2 5
Testing for rare conditions 1,492 1 15 60 2 2 4
Comparing classifier performance with baselines 1,220 23 27 77 0 0 7
Errors in predictor variables 1,089 8 1 19 0 0 1
2,912,071 1,012 53 83 3,925 5,521


%NM percentile rank (avg 53) for the article of tracked articles of a similar age in Nature Methods.

%ALL percentile rank (avg 83) for the article of tracked articles of a similar age in all journals.

Total accesses 2,912,071.

Total citations 5,538 = sum(maxi(webscience,crossref)).

news + thoughts

Nasa to send our human genome discs to the Moon

Sat 23-03-2024

We'd like to say a ‘cosmic hello’: mathematics, culture, palaeontology, art and science, and ... human genomes.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
SANCTUARY PROJECT | A cosmic hello of art, science, and genomes. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
SANCTUARY PROJECT | Benoit Faiveley, founder of the Sanctuary project gives the Sanctuary disc a visual check at CEA LeQ Grenoble (image: Vincent Thomas). (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
SANCTUARY PROJECT | Sanctuary team examines the Life disc at INRIA Paris Saclay (image: Benedict Redgrove) (details)

Comparing classifier performance with baselines

Sat 23-03-2024

All animals are equal, but some animals are more equal than others. —George Orwell

This month, we will illustrate the importance of establishing a baseline performance level.

Baselines are typically generated independently for each dataset using very simple models. Their role is to set the minimum level of acceptable performance and help with comparing relative improvements in performance of other models.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Comparing classifier performance with baselines. (read)

Unfortunately, baselines are often overlooked and, in the presence of a class imbalance5, must be established with care.

Megahed, F.M, Chen, Y-J., Jones-Farmer, A., Rigdon, S.E., Krzywinski, M. & Altman, N. (2024) Points of significance: Comparing classifier performance with baselines. Nat. Methods 20.

Happy 2024 π Day—
sunflowers ho!

Sat 09-03-2024

Celebrate π Day (March 14th) and dig into the digit garden. Let's grow something.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2024 π DAY | A garden of 1,000 digits of π. (details)

How Analyzing Cosmic Nothing Might Explain Everything

Thu 18-01-2024

Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
How Analyzing Cosmic Nothing Might Explain Everything. Text by Michael Lemonick (editor), art direction by Jen Christiansen (Senior Graphics Editor), source: SDSS

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”

voids

Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.

Sloan Digital Sky Survey Data Release 12

constellation figures

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)

stars

Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).

cosmology

H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
Google whack “vicissitudinal corporealization”
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