I've read through the infamous UK Home Office grooming report.
This is routinely used by MSM to deflect attention from groups who committed crimes against young girls in towns across the UK.
Let's go through the statistical tricks used in this deeply flawed analysis.
Thread 🧵
This is routinely used by MSM to deflect attention from groups who committed crimes against young girls in towns across the UK.
Let's go through the statistical tricks used in this deeply flawed analysis.
Thread 🧵
The report is, quite literally, a whitewash.
It uses a standard set of statistical tricks to avoid a stark and glaring conclusion.
That there was a sustained, targeted and focussed attack by groups of Pakistani men on white working class and Sikh girls across the UK.
It uses a standard set of statistical tricks to avoid a stark and glaring conclusion.
That there was a sustained, targeted and focussed attack by groups of Pakistani men on white working class and Sikh girls across the UK.
1. Choose limited data points
One way to force a conclusion is to only range over a small and carefully chosen set of data points.
In this case, the report used a limited number of use cases which largely ignored the high profile grooming gangs of Pakistani origin.
One way to force a conclusion is to only range over a small and carefully chosen set of data points.
In this case, the report used a limited number of use cases which largely ignored the high profile grooming gangs of Pakistani origin.
Only Rotherham was considered in earnest, as it could hardly be ignored.
A typical trick is to only use one troublesome dataset - generally the most high profile one. This gives plausible denial on accusations of manipulation of source data.
This is called “the domain trick”
A typical trick is to only use one troublesome dataset - generally the most high profile one. This gives plausible denial on accusations of manipulation of source data.
This is called “the domain trick”
2. Quotable tautologies
A tautology is a statement that is true by virtue of its logical form. I.e. a statement that is inevitably true.
Such statements are not worth declaring as they have no statistical significance.
“Most perpetrators were white” is such a statement.
A tautology is a statement that is true by virtue of its logical form. I.e. a statement that is inevitably true.
Such statements are not worth declaring as they have no statistical significance.
“Most perpetrators were white” is such a statement.
In a broadly white country, this statement is tautological. The real question is, were other groups present in a statistically significant way?
I.e. were they over-represented in the group of perpetrators, given their preponderance in the population as a whole?
I.e. were they over-represented in the group of perpetrators, given their preponderance in the population as a whole?
The whole point of data analysis is to uncover statistically significant results.
There was such no analysis in this report.
This trick does, however, produce lovely quotable headlines that will reassure people of their own biases.
This is called “the pericope trick”
There was such no analysis in this report.
This trick does, however, produce lovely quotable headlines that will reassure people of their own biases.
This is called “the pericope trick”
3. Blame data quality
Whenever you want to exclude a data item, you can just blame data quality.
In this case, they often cited data quality of ethnicity classifications.
The UK ethnicity enumeration is flawed in many ways.
Whenever you want to exclude a data item, you can just blame data quality.
In this case, they often cited data quality of ethnicity classifications.
The UK ethnicity enumeration is flawed in many ways.
Using it, for example, it is not possible to make a distinction between a native Briton and an East European - or a West African and a South African.
These flaws make it a poor tool for a lot of crime analysis.
These flaws make it a poor tool for a lot of crime analysis.
However, ironically, the enumeration is strong in the context of South Asian countries. Where ethnicity is broken down into fine detail.
It's simple to identify Pakistanis, but the HO claimed data quality on the whole set and didn’t go there.
This is called “the quality trick”
It's simple to identify Pakistanis, but the HO claimed data quality on the whole set and didn’t go there.
This is called “the quality trick”
4. Data ranges as data points
In any given set of statements, there will be a range of values. Each item in the range will then have a corresponding frequency count. So some items might be present once and others hundreds of times.
In any given set of statements, there will be a range of values. Each item in the range will then have a corresponding frequency count. So some items might be present once and others hundreds of times.
A common trick, though, is to simply publish the range as a flat list without frequencies.
“Perpetrators come from a range of countries” is this trick in action.
It presents the range of nationalities without being concerned with how each presents itself statistically.
“Perpetrators come from a range of countries” is this trick in action.
It presents the range of nationalities without being concerned with how each presents itself statistically.
The Home Office report lists Portuguese alongside Pakistani in a flat list - which, of course, means nothing at all in terms of statistical relevance.
This is called “the range trick”
This is called “the range trick”
5. Mixup up data definitions
A final trick I will mention is to mix up the definition of data types and data classes.
You can, for example, deliberately use nationality in place of ethnicity when it suits your cause.
A final trick I will mention is to mix up the definition of data types and data classes.
You can, for example, deliberately use nationality in place of ethnicity when it suits your cause.
Tripping between the two definitions, so that the results always play in favour of your pre-agreed hypothesis.
This is called “the predicate trick”
This is called “the predicate trick”
Data will tell any story you want.
When I am tasked with analysing data, I often ask “What do you want the data to say?”.
Letting data speak for itself, teasing out its hidden truths, allowing it to reveal its story, hardly ever happens - especially in the public sector.
When I am tasked with analysing data, I often ask “What do you want the data to say?”.
Letting data speak for itself, teasing out its hidden truths, allowing it to reveal its story, hardly ever happens - especially in the public sector.
Let me leave the last word to, of all people, Elvis:
“Truth is like the sun. You can shut it out for a time, but it ain't goin' away.”
“Truth is like the sun. You can shut it out for a time, but it ain't goin' away.”
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