AI has its issues and it can raise concern such as is it putting people out of work, However it can prove a powerful tool in evaluating evidence and finding bits of research that might be difficult to find. I often think of it as "Alien Intelligence," as it is much different to human intelligence.
What I have done here is ask three questions. The first one is a real quote from DEFRA's guidance on ragwort control and it is the absolutely key factor in how they establish and promote the amount and rate of ragwort poisoning. The "he" in the quote is Professor Derek Knottenbelt who has also been quoted rather ridiculously as claiming that ragwort is poisoning the Cinnabar moth, which I hasten to say is NOT the main reason that ragwort is an ecologically valuable wildflower.
Here I have used ChatGPT as an independent evaluator of the policy. At first it points out the strength and weaknesses of DEFRA's statements. Then, when it is supplied the actual data obtained from Freedom of Information requests it points out it is completely wrong to use the data and when I tell it that the sentence is from government advice then it becomes really scathing about the bad science. I quote the responses entirely as a cut and paste from the output of this clever computer program. I have only labelled the questions for clarity
Question 1
What can you tell me about the logic and reasoning in the following statement? "He has estimated a figure of 500 horse deaths from ragwort poisoning in 2000. This figure is based on the number of confirmed horse deaths from ragwort poisoning seen by the Philip Leverhulme Large Animal Hospital Teaching Hospital at Liverpool University as a percentage of all the horse cases treated during the ear, and grossed up to be representative of the total horse population."
ChatGPT said:
The statement presents an estimate of horse deaths from ragwort poisoning using inductive reasoning based on extrapolation. Here's a breakdown of the logic and reasoning behind it, including both its strengths and potential flaws:
🔍 Summary of the Logic
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Observed Data Source:
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A specific number of confirmed horse deaths from ragwort poisoning were observed at a single veterinary hospital (Liverpool University's Philip Leverhulme Large Animal Teaching Hospital).
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Proportional Estimation:
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This number is taken as a proportion of total horse cases treated at that hospital during 2000.
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Extrapolation:
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The proportion is then "grossed up" (i.e., scaled) to estimate the number of deaths across the entire horse population in the UK (or another broader context).
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✅ Logical Strengths
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Use of empirical data: The estimate is grounded in actual confirmed cases, not speculation.
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Methodical scaling: Applying a proportion to a larger population is a common statistical technique when comprehensive data isn't available.
❌ Logical Weaknesses and Concerns
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Sampling Bias:
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The Liverpool hospital's cases may not be representative of the whole UK horse population.
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Horses seen at a teaching hospital could have more severe or unusual conditions, potentially overestimating rare events like ragwort poisoning.
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Confirmation Bias:
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Only confirmed cases were counted. Ragwort poisoning is difficult to diagnose definitively, so the actual number of cases may be underreported, or misdiagnosed, affecting reliability.
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Assumption of Uniformity:
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It assumes the same rate of ragwort poisoning applies uniformly across all horse populations in different regions, which is unlikely (as environmental exposure varies).
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Small Sample Size Risk:
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If the number of observed cases was small, the resulting estimate could have high variance and large margins of error when extrapolated.
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🧠 Type of Reasoning
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Inductive Reasoning: Drawing general conclusions (nationwide deaths) from specific observations (hospital cases).
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Statistical Estimation: Involves proportional scaling and population inference.
🧮 Example Analogy
Imagine seeing 2 red cars in a dealership that sells 10 cars and concluding that 20% of all cars in the country are red, then scaling that to millions of cars. That only works if your sample (the dealership) is representative — otherwise, your extrapolation may be way off.
📌 Conclusion
The reasoning shows a structured attempt to estimate a figure from limited data, but it relies heavily on the representativeness of a single data source, which is a major potential flaw. It's a reasonable starting point, but not robust enough to be definitive without additional, more representative data.
Question 2
You said:
ChatGPT said:
If a Freedom of Information (FOI) request to the hospital reveals only one case of ragwort poisoning in an eight-year period, it severely undermines the credibility and validity of the original estimate of 500 horse deaths in a single year. Here's a detailed analysis of what that implies:
🔍 What This New Information Reveals
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Factual Discrepancy:
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The original estimate was supposedly based on a meaningful number of confirmed deaths at that hospital in 2000.
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But if only one case occurred over eight years, then the hospital could not possibly have had a significant enough sample in 2000 to justify such extrapolation.
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Invalid Assumptions:
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The method described (taking the hospital’s confirmed ragwort deaths as a proportion and scaling up) relies on false or exaggerated input data.
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This suggests that either:
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The original estimator misunderstood or misrepresented the hospital data.
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Or the methodology was used in bad faith (deliberately misleading).
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❌ Logical and Ethical Consequences
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Flawed Reasoning:
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The extrapolation logic collapses entirely if the base figure is incorrect or negligible.
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Inflated Estimate:
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The claim of 500 deaths is now shown to be not just imprecise but likely unfounded.
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Potential Misinformation:
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Such a statement, if made in a policy or advocacy context, could be highly misleading, leading to poor regulation or misplaced public concern.
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🧠 Implications for Critical Thinking
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Always check primary sources: This is a clear example of why it’s essential to verify claims, especially when they're used to support large-scale generalizations.
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Be skeptical of gross-ups: Scaling from small or non-representative data to national figures must be done with rigorous checks, transparency, and peer review.
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Use FOI data carefully: FOI responses, being from authoritative sources, are strong evidence — and in this case, they directly contradict the original narrative.
🧾 Summary Judgment
The FOI response strongly suggests the original estimate of 500 deaths due to ragwort poisoning in 2000 is invalid. It exposes either a methodological failure or a misrepresentation of data, making the figure deeply unreliable and potentially misleading.
If you're investigating this further or challenging a claim, this FOI result is a powerful rebuttal.
Question 3
You said:
ChatGPT said:
If the original quote estimating 500 horse deaths from ragwort poisoning in 2000 comes directly from government advice, yet a Freedom of Information request shows only one case at the referenced hospital over eight years, then this raises serious concerns on multiple levels:
⚖️ Implications of a Government-Originated Estimate
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Government Credibility at Stake:
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Government-issued advice is expected to be evidence-based, transparent, and reliable.
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If it includes a wildly inflated estimate based on non-existent or misused data, it damages public trust in that institution and its policies.
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Policy and Regulation Impact:
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This estimate likely influenced ragwort control legislation, land management obligations, or public guidance to landowners.
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Policies based on false or unsupported figures may be disproportionate, costly, or even unjustified.
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Misuse of Authority:
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Using an authoritative tone (e.g., citing a university hospital) to support a claim that is not factually accurate borders on misinformation, whether intentional or negligent.
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🧠 Analytical Breakdown
Let’s connect the pieces:
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Claim: 500 deaths based on extrapolated data from Liverpool University's animal hospital.
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Reality (via FOI): Only one case in eight years—not even one per year.
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Logical consequence: The core data point used to justify the extrapolation never existed.
This suggests one of the following:
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The estimation method was fabricated or misunderstood.
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The government relied on poorly vetted secondary sources.
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There was a deliberate inflation to justify a policy objective.
🧾 Accountability Questions
This scenario invites tough questions for policymakers:
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Who created this estimate?
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Was the source peer-reviewed or independently verified?
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Why was the estimate not updated or withdrawn when more accurate data became available?
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How was the FOI data reconciled with the original policy narrative?
💡 Broader Takeaway
This is a textbook example of why transparency, evidence verification, and skepticism are essential in interpreting government guidance — especially when it leads to regulation. It shows how bad data, once institutionalized, can cascade into widespread misinformation and skew public perception for years.
✅ Conclusion
If a government document made this claim, yet the cited data source (the hospital) reports only one case in eight years, then the claim is:
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Demonstrably false or grossly exaggerated,
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Potentially misleading, and
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In need of correction or retraction.
It may justify a formal challenge or review of the guidance that relied on it.
