statistics
+ data
Nature Methods: Points of Significance
Generated on 27-Nov-2024 (7 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
Mon 16-09-2024
I donât 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.
▲ 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.
Sat 23-03-2024
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▲ SANCTUARY PROJECT | A cosmic hello of art, science, and genomes.
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▲ SANCTUARY PROJECT | Benoit Faiveley, founder of the Sanctuary project gives the Sanctuary disc a visual check at CEA LeQ Grenoble (image: Vincent Thomas).
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▲ SANCTUARY PROJECT | Sanctuary team examines the Life disc at INRIA Paris Saclay (image: Benedict Redgrove)
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Fri 22-03-2024
All animals are equal, but some animals are more equal than others. âGeorge 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.
▲ Nature Methods Points of Significance column: Comparing classifier performance with baselines.
(
read)
Unfortunately, baselines are often overlooked and, in the presence of a class imbalance, 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 21:546–548.
Sat 09-03-2024
Celebrate Ï Day (March 14th) and dig into the digit garden. Let's grow something.
▲ 2024 Ï DAY | A garden of 1,000 digits of Ï.
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details)
