Posts Tagged ‘stats’

Yale COVID-19 Statistics | COVID-19: Yale Actions and Response

August 23, 2020

Countries beating Covid-19 — Untitled

July 7, 2020

Countries beating Covid-19 — Untitled

CT Coronavirus Deaths Hit 3,285: Town-By-Town | Across Connecticut, CT Patch

May 16, 2020

Parts of the table in the article are excerpted below, focussing on the area around New Haven & the Shoreline.

Comparatively few deaths in New Haven itself relative to the number of cases – in contrast to Branford & Hamden.

Town Deaths Cases
Branford 38 295
Essex 1 17
Fairfield 92 513
Greenwich 43 747
Guilford 8 89
Hamden 81 835
Madison 17 119
New Haven 83 2,126
North Haven 19 211
Westport 20 266
Woodbridge 27 108

What Is the Real Coronavirus Toll in Each State? – The New York Times

May 6, 2020

Coronavirus: Mapping Covid-19 Confirmed Cases and Deaths Globally

April 26, 2020

COVID-19/Coronavirus Real Time Updates With Credible Sources in US and Canada | 1Point3Acres

April 26, 2020

Forecasting s-curves is hard – Constance Crozier

April 19, 2020

The Two Settings of Kind and Wicked Learning Environments

April 17, 2020

There’s a paper on this topic that introduced the idea of “kind and wicked learning environments”:

…in wicked environments it is difficult to do inference based on data. One solution seems to be to break down the problem in such a way that you can observe sub-problems in a kind environment.

The Two Settings of Kind and Wicked Learning Environments

Robin M. Hogarth1, Tomás Lejarraga2, and Emre Soyer3

QT:{{” Inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches. This conceptual framework facilitates identifying sources of inferential errors and can be used, among other things, to suggest how to target corrective procedures. For example, structuring learning environments to be kind improves probabilistic judgments. Potentially, it could also enable economic agents to exhibit maximizing behavior.

Interpreting odds and odds ratios – The Stats Geek

November 18, 2018

Big names in statistics want to shake up much-maligned P value

August 8, 2017

Big names in #statistics want to shake up…#Pvalue Stronger significance cutoffs (.005?) but danger of FNs

“Lowering P-value thresholds may also exacerbate the “file-drawer problem”, in which studies with negative results are left unpublished, says Tom Johnstone, a cognitive neuroscientist at the University of Reading, UK. But Benjamin says all research should be published, regardless of P value.

Other scientific fields have already cracked down on P values — and in 2015, one psychology journal banned them. Particle physicists, who collect reams of data from atom-smashing experiments, have long demanded a P value below 0.0000003 (or 3 × 10−7) because of concerns that a lower threshold could lead to mistaken claims, notes Valen Johnson, a statistician at Texas A&M University in College Station and a co-lead author of the paper. More than a decade ago, geneticists took similar steps to establish a threshold of 5 × 10−8 for
genome-wide association studies, which look for differences between people with a disease and those without across hundreds of thousands of DNA-letter variants.”