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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 27-Nov-2024 (125 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 227,000 56 90 98 112 93 89
Error bars 223,000 55 98 99 186 179 272
Visualizing samples with box plots 205,000 52 86 98 390 371 77
Principal component analysis 203,000 75 87 96 513 867 87
Association, correlation and causation 187,000 56 98 99 215 179 187
Replication 135,000 36 57 93 127 94 24
P values and the search for significance 134,000 46 93 98 64 71 151
Statistics versus machine learning 130,000 53 97 99 224 888 348
Importance of being uncertain 121,000 29 79 96 63 55 51
Power and sample size 110,000 27 74 97 138 126 54
Nonparametric tests 66,000 17 53 87 60 54 12
Comparing samples—part I 64,000 16 67 92 29 33 20
Analysis of variance and blocking 62,000 16 60 91 51 49 18
Nested designs 61,000 16 86 96 44 38 55
Bayes' theorem 59,000 17 72 94 67 53 36
Two-factor designs 59,000 16 34 51 28 27 3
The SEIRS model for infectious disease dynamics 58,000 35 98 99 117 127 356
Comparing samples—part II 55,000 14 48 87 54 48 12
Simple linear regression 54,000 16 74 93 78 83 28
Split plot design 54,000 15 40 78 52 46 7
Sources of variation 54,000 15 53 91 19 18 16
Bayesian statistics 49,000 14 55 89 21 17 16
Designing comparative experiments 49,000 13 51 82 13 14 8
Sampling distributions and the bootstrap 48,000 14 54 91 84 77 18
Interpreting P values 47,000 17 74 95 50 53 57
Multiple linear regression 47,000 14 80 95 88 80 42
Classification and regression trees 45,000 17 45 79 194 219 9
Classification evaluation 40,000 13 70 94 227 208 44
Model selection and overfitting 39,000 13 66 92 392 347 26
Bayesian networks 38,000 11 45 77 38 26 7
Clustering 34,000 12 50 89 86 99 21
Optimal experimental design 28,000 12 49 89 58 67 20
Analyzing outliers: influential or nuisance? 27,000 9 28 65 61 56 4
Machine learning: supervised methods 23,000 9 56 91 173 203 22
Logistic regression 22,000 7 67 92 77 67 25
Regression diagnostics 22,000 7 46 72 53 48 6
The curse(s) of dimensionality 21,000 9 58 88 207 228 20
Ensemble methods: bagging and random forests 19,000 7 35 78 147 162 9
Modeling infectious epidemics 18,000 11 85 97 54 67 115
Machine learning: a primer 17,000 7 89 97 103 112 74
Tabular data 14,000 5 28 64 3 2 4
Regularization 13,000 4 53 86 34 27 14
Two-level factorial experiments 11,000 5 12 24 11 11 1
Predicting with confidence and tolerance 8,183 4 24 59 6 6 4
Markov models—Markov chains 7,993 4 41 90 18 19 24
Markov models — hidden Markov models 6,183 3 26 79 19 24 10
The standardization fallacy 5,278 4 47 92 30 32 28
Convolutional neural networks 5,268 11 15 69 17 20 5
Quantile regression 4,398 2 36 78 39 73 9
Markov models — training and evaluation of hidden Markov models 4,285 2 40 84 5 5 12
The class imbalance problem 4,267 4 20 73 42 51 7
Survival analysis—time-to-event data and censoring 3,974 5 17 68 12 11 4
Uncertainty and the management of epidemics 3,671 2 21 70 6 9 7
Analyzing outliers: robust methods to the rescue 3,665 2 25 50 20 18 3
Neural networks primer 3,210 5 16 67 4 5 4
Graphical assessment of tests and classifiers 2,245 2 26 82 8 6 11
Regression modeling of time-to-event data with censoring 2,052 3 12 71 4 5 5
Comparing classifier performance with baselines 1,701 7 28 76 0 0 7
Testing for rare conditions 1,553 1 15 60 3 3 4
Errors in predictor variables 1,345 4 3 30 1 1 1
Propensity score matching 1,102 15 16 57 0 0 2
3,062,373 988 52 82 5,039 5,977


%NM percentile rank (avg 52) for the article of tracked articles of a similar age in Nature Methods.

%ALL percentile rank (avg 82) for the article of tracked articles of a similar age in all journals.

Total accesses 3,062,373.

Total citations 6,281 = sum(maxi(webscience,crossref)).

news + thoughts

Propensity score weighting

Mon 17-03-2025

The needs of the many outweigh the needs of the few. —Mr. Spock (Star Trek II)

This month, we explore a related and powerful technique to address bias: propensity score weighting (PSW), which applies weights to each subject instead of matching (or discarding) them.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Propensity score weighting. (read)

Kurz, C.F., Krzywinski, M. & Altman, N. (2025) Points of significance: Propensity score weighting. Nat. Methods 22:1–3.

Happy 2025 π Day—
TTCAGT: a sequence of digits

Thu 13-03-2025

Celebrate π Day (March 14th) and sequence digits like its 1999. Let's call some peaks.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2025 π DAY | TTCAGT: a sequence of digits. The digits of π are encoded into DNA sequence and visualized with Sanger sequencing. (details)

Crafting 10 Years of Statistics Explanations: Points of Significance

Sun 09-03-2025

I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player

Points of Significance is an ongoing series of short articles about statistics in Nature Methods that started in 2013. Its aim is to provide clear explanations of essential concepts in statistics for a nonspecialist audience. The articles favor heuristic explanations and make extensive use of simulated examples and graphical explanations, while maintaining mathematical rigor.

Topics range from basic, but often misunderstood, such as uncertainty and P-values, to relatively advanced, but often neglected, such as the error-in-variables problem and the curse of dimensionality. More recent articles have focused on timely topics such as modeling of epidemics, machine learning, and neural networks.

In this article, we discuss the evolution of topics and details behind some of the story arcs, our approach to crafting statistical explanations and narratives, and our use of figures and numerical simulations as props for building understanding.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Crafting 10 Years of Statistics Explanations: Points of Significance. (read)

Altman, N. & Krzywinski, M. (2025) Crafting 10 Years of Statistics Explanations: Points of Significance. Annual Review of Statistics and Its Application 12:69–87.

Propensity score matching

Mon 16-09-2024

I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player

In many experimental designs, we need to keep in mind the possibility of confounding variables, which may give rise to bias in the estimate of the treatment effect.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Propensity score matching. (read)

If the control and experimental groups aren't matched (or, roughly, similar enough), this bias can arise.

Sometimes this can be dealt with by randomizing, which on average can balance this effect out. When randomization is not possible, propensity score matching is an excellent strategy to match control and experimental groups.

Kurz, C.F., Krzywinski, M. & Altman, N. (2024) Points of significance: Propensity score matching. Nat. Methods 21:1770–1772.

Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
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