Where are the numbers? by Norman Fenton and Martin Neil

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Sitemap - 2020 - Where are the numbers? by Norman Fenton and Martin Neil

On false positives in COVID19 testing again: we are being misled over confirmatory testing

COVID-19 in the UK: the remarkable divergence between number of 'cases' and number of people reporting symptoms

Pooled COVID19 testing makes the data on 'cases' even more dubious

As London goes into Tier 4 COVID lockdown here is proof that the government data for London is flawed

UK Covid Testing data: Remarkable relationship between number of tests and positivity rate when we drill down to regions

We still are not getting the most basic data needed about COVID-19 testing

Latest UK COVID-19 stats roundup

No - there is nothing especially unusual about this lottery outcome

Statistical analyses attempting to determine election fraud: the need for a causal framework

How to explain an increasing proportion of people testing positive for COVID if there is neither an increase in proportion of genuine cases nor increase in the false positive rate

Explaining away, augmentation, and the assumption of independence

Nudge, nudge say no more*: Learning from behavioural changes that fail

Time to demand the evidence to support continued COVID19 lockdowns and restrictions

Why we know so little about COVID-19 from testing data - and why some extra easy-to-get data would make a big difference

COVID19 Hospital admissions data: evidence of exponential increase?

Is there bias in VAR decisions? a simple statistical analysis

A critical flaw in the Government's official daily "UK COVID hospital admissions" data

The impact of false positives in Covid testing

Don’t Panic: Limits to what we know about UK Covid-19 PCR testing, inferred infection rates and the rate of false positives

New paper highlights serious limitations of using the likelihood ratio for mixture DNA profile matching

UK: Plotting new Covid cases per 1000 tested

The Cab accident problem: new insights into probabilistic reasoning when there is uncertainty about the witness reliability

COVID19 trend plots: deaths by numbers tested

A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing

The need for causal models to understand and explain whether statistics provide evidence of racially biased policing

UK Covid19 death rates by religion: Jews by far the highest and atheists by far the lowest 'overall' - but what does it mean?

Bayesian networks in healthcare

When 'dependent' expert reports might be more informative than independent ones

Covid-19: Infection rates are higher, fatality rates lower than widely reported

Why most studies into COVID19 risk factors may be producing flawed conclusions - and how to fix the problem

Covid-19 risk for the black and minority ethnic community: why reports are misleading and create unjustified fear and anxiety

Basic training with a Bayesian network tool helps lay people solve complex problems

Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics

Coronavirus: country comparisons are pointless unless we account for these biases in testing (reprint of our article in The Conversation)

COVID-19: the need for more random testing combined with causal modelling

In the UK football was always going to be the tipping point for Coronavirus risk mitigation

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