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If asked to describe science in one sentence, I would call
it a more or less formalized method for finding objective knowledge. Yet, what
passes for “science” today – climate science, air pollution, species
extinctions, epidemiology, medical statistics and more – seems to be geared up
to produce, not knowledge, but propaganda that supports dubious political narratives.
This is, in part, a re-work of an article I published in
2018: [[i]].
But I will also look at how well several areas of “science” today measure up to
the standards of science as it should be. And end with some – very good – news
from across the pond.
What is science?
Oxford Languages defines science as “the systematic study
of the structure and behaviour of the physical and natural world through
observation, experimentation, and the testing of theories against the evidence
obtained.” This, I think, is basically a good definition; although it does,
perhaps, miss the possibility of using science to study human behaviours. Most important,
though, is the final component of the methodology: testing ideas against
evidence.
The scientific method
Science, as we know it, began in the late 16th
and 17th centuries; between the Renaissance and the Enlightenment.
The work of people like Galileo and Francis Bacon led to changes in how we
examined the reality around us. Which, in turn, led to a great increase in the
level of our knowledge about our surroundings and about ourselves.
Properly done, science is conducted according to a procedure
known as the scientific method. The details may vary a little from one
discipline to another; but the basic scheme is the same. Here’s a brief outline
of the steps within the scientific method:
1)
Pose a question, to which you want to find an
answer.
2)
Do background research on that question.
3)
Construct a hypothesis. This is a statement,
giving a possible answer to your question. In some circumstances, you may want
to take someone else’s hypothesis for re-testing.
4)
Develop testable predictions of your hypothesis.
For example: “If my hypothesis is true, then when X happens, Y will happen more
often than it does when X doesn’t happen.”
5)
For each prediction, formulate an appropriate
null hypothesis, against which you will test your prediction. For example: “Whether
X happens doesn’t influence whether or not Y happens.”
6)
Test the predictions against their null
hypotheses by experiment or observation. If you need to use someone else’s data
as part of this, you must first check the validity of their data.
7)
If the test does not support the predictions of
your hypothesis, you must consider this as a victory for the null hypothesis.
8)
Collect your results, and check they make sense.
If not, troubleshoot.
9)
Analyze your results and draw conclusions. This
may require the use of statistical techniques.
10) Repeat
for each of the predictions of your hypothesis.
11) If
the results wholly or partially negate your hypothesis, modify your hypothesis
and repeat. In extreme cases, you may need to modify the original question,
too.
12) If
the results back up your hypothesis, that strengthens your hypothesis.
13) If
negative results falsify your hypothesis, that weakens or destroys the
hypothesis.
Key steps within the scientific method are: The
construction of the right hypothesis, and of the null against which it is
tested. The making of testable predictions of the hypothesis, and their
testing. And the feedback loop, which – using the results found – strengthens,
weakens or modifies your hypothesis.
I see the construction of the null hypothesis, to be
upheld when a prediction fails, as one of the most important steps in this
procedure. I think of the null hypothesis in science as like the presumption of
innocence in criminal law! As a scientist, you should always be trying to catch
yourself out – so you don’t make a mistake that might have serious
consequences.
Rules for the conduct of science
It’s very easy to get science wrong. There’s always a
possibility of error in your measurements, statistics, or deductions. Or of
insufficiently rigorous testing or sampling. Or of bias, whether conscious or
unconscious.
To minimize the chances of getting science wrong, and to
enable others to build on its results, there are a number of rules of conduct
which scientists are expected to follow. Here is a list of some of them:
1)
Any hypothesis that is put forward must be
falsifiable. If there’s no way to disprove a hypothesis, it isn’t a scientific
one.
2)
Data must not be doctored. Any necessary
adjustments to raw data, and the reasoning behind them, must be fully and
clearly documented.
3)
Data must not be cherry picked to achieve a
result. Data that is valid, but goes against a desired result, must not be
dropped.
4)
Graphs or similar devices must not be used to
obfuscate or to mislead.
5)
Enough information must be supplied to enable
others to replicate the work if they wish.
6)
Scientists must be willing to share their data.
And code, too, when code is involved.
7)
Supplementary information, such as raw data,
must be fully and promptly archived.
8)
To identify and quantify the error bars on
results is important. (For example, by stating the range within which there’s a
95% chance that a value being measured lies.)
9)
Uncertainties are important, too. They must be
clearly identified and, if possible, estimated.
10) Above
all, the conduct of science must be honest and unbiased. In a nutshell: If it
isn’t honest, it isn’t science. It’s nonscience (rhymes with
conscience).
A failure to obey one or more of these rules of conduct
doesn’t necessarily mean the science is bad. However, it does raise a red flag;
particularly if there may be a suspicion of bias or dishonesty. And if a
sufficiently skilled person, with sufficient time to spare, doesn’t have enough
information to check the validity of a scientific paper, or to attempt to
replicate the work it describes, there’s a very good chance the science in it
is bad.
To sum up
When done properly, science is, as I said earlier, a more or
less formalized method for finding objective knowledge. But if science is to be
done properly, it must be done with total honesty. Activities that look like
science, yet do not follow the scientific method honestly, or do not give sufficient
detail to let others seek to replicate the work, are not science. They are nonscience.
Evidence for corruption of science
Today, though, we frequently see “scientists” publishing
results that seem to support a political narrative, rather than contributing –
as science should – to the furthering of objective human knowledge. We see
reports being collated by bodies that obviously have a political agenda, in an
attempt to abet the making of policies that further that agenda. We see
political bodies seeking to suppress information, that might lead people to
question official narratives. And we see scientists, who try to apply real
science in areas affected by such fixing, being wrongly denied publication or
funding, or even having to leave the scientific area altogether.
I will give you just five examples of these syndromes.
Climate “science”
I have already published a brief de-bunk of the “climate
crisis” meme, here: [[ii]].
And I have documented the history of the climate agenda, and the green agenda
as a whole, in a series of three articles: [[iii]],
[[iv]],
[[v]].
I told how the United Nations and its Intergovernmental Panel on Climate Change
(IPCC) has co-opted many scientists into lending their weight to the scares
about climate change. They have, in effect, misused the authority of science to
mislead people into believing that there is more truth to the climate scares
than there really is.
I did not mention there that the primary mechanism, by which
climate “scientists” influence public perceptions, is through the outputs of
their computer models, called AOGCMs (Atmosphere-Ocean General Circulation
Models). Now, computer models can be useful tools, particularly for exploring
“what-if” situations. But their outputs are not data. And they should never be
treated as if they were data. Moreover, before any model can be used to explore
new situations, it must be thoroughly tested, to find out how well its
predictions match up with the reality when it eventuates.
Yet there is an astonishing lack of published material on
the results of testing AOGCMs against measured data. They may, indeed, have
been tested by “hindcasting,” using past climate data. But the crucial test, of
model predictions against measurements made at the times the modellers
expected their predictions to be fulfilled, seems to be rarely carried out. And
very rarely reported.
Indeed, I have only been able to find one recent example of
such a test: [[vi]].
Now, this is not a peer-reviewed paper, only a scientist’s blog post. But Dr
Roy Spencer, the meteorologist author, is a world expert on satellite
temperature measurements. His statement that “all 39 climate models exhibit
larger warming trends than all three classes of observational data” does,
therefore, carry weight. And his graph shows that more than half the models
predict trends greater by 50% or more than those observed.
You would have expected that such tests of climate models
would be routinely carried out, and published in prestigious scientific
journals for all to see. But not so. It is hard not to suspect that this is an
example of corruption of science in service of political agendas.
Air pollution toxicology
I have written on the history of air pollution toxicology in
the UK, here: [[vii]].
I told of COMEAP’s (the Committee on Medical Effects of Air Pollution’s) 2009
report, which after skating all around the real problem, came down on the side
of numbers provided by the UN’s World Health Organization (WHO). Of their 2018
report on nitrogen dioxide (NO2), in which the views of three
dissenting experts, who maintained (correctly, in my view) that there was
insufficient evidence on which to give a figure for the toxicity of NO2,
were ignored. And of political interference by a deputy mayor of London with a
2018 study that concluded there was “no evidence of a reduction in the
proportion of children with small lungs” over the period of the study.
Again, I cannot escape the feeling that in these instances,
there was corruption of science in the service of politics. In this case, the
agenda is to progressively force out of our cars those of us who drive older
cars, and cannot afford either to upgrade them or to pay ULEZ fees or
equivalent.
Species extinctions
The UN has its own counterpart to the IPCC in this area.
This is IPBES (Intergovernmental Science-Policy Platform on Biodiversity and
Ecosystem Services). IPBES issued a 2019 report claiming that impacts of human
activities were threatening a million species with extinction. Yet I myself
have several times asked environmentalists to name one species to whose
extinction I have contributed, and to say what I did, and roughly when, towards
that extinction. I’ve never received any answer.
But just recently, the Royal Society has published a new report:
[[viii]].
Its abstract tells us that: “Extinction rates have increased over the last five
centuries, but generally declined in the last 100 years. Recent extinctions
were predominantly on islands, whereas the majority of non-island extinctions
were in freshwater.” And their graph (Figure 2(b)) shows extinctions peaking at
about 50 per decade in the 1870s, 1930s and 1970s/80s, but dropping rapidly off
since then. It seems, then, that the hype about a million species being
threatened with extinction, whatever it was based on, certainly wasn’t founded on
science.
Epidemiology
The science of epidemiology will forever be scarred by the
antics of “Professor Lockdown,” Neil Ferguson of Imperial College London. In
October 2020, I wrote an essay about SAGE, the UK’s “Scientific Advisory Group
for Emergencies,” which was heavily involved in lobbying for draconian COVID
lockdowns. It is here: [[ix]].
I think it is worth repeating what I said there about Professor Ferguson’s past
statements.
“The British response [the first lockdown], Ferguson said on
March 25th, makes him ‘reasonably confident’ that total deaths in
the United Kingdom will be held below 20,000.” October 15th,
cumulative deaths: 43,293 and counting. On August 17th, he was “‘optimistic’
Europe won’t see very large numbers of new COVID-19 cases this year.” October
15th, daily new case count: 18,980. That’s 2.4 times the peak of
7,860 on April 10th. Then, on September 22nd, we had this headline in the Sun: “Professor
Lockdown doubles down on 500k UK coronavirus deaths forecast [from March] – and
claims it was ‘underestimate’.”
Far be it from me to be kind to climate “scientists,” but it
is fair to say that the equations of epidemiology are orders of magnitude
simpler than the equations of climate science.
Medical statistics
And then there’s the infamous “excess deaths” saga,
involving the Office for National Statistics (ONS). I wrote about this at [[x]].
Here are some quotes.
“They [the ONS] re-defined the way in which excess deaths
are to be calculated. In a way that seems to have greatly reduced the resulting
numbers, and thus the apparent size of the ongoing ‘excess deaths’ problem.
While breaking the link between their figures and hard evidence from the real
world.” Further: “The reactions which Andrew Bridgen received when he first
brought up the subject in parliament suggest that someone, or some group, in a
very high position does NOT want the full truth about excess deaths in the UK
since 2020 brought out into the open.” And I ended with: “The dog has eaten my
data. It looks as if the entire world may have stopped providing any excess
mortality figures which are founded on real-world evidence! Cynical me does not
think this is a co-incidence.”
I strongly suspect that the heavy hand of the WHO has been
in here, too.
Why has science become corrupted?
So, why do not only many individual scientists, but also the
entire process of science in many fields highly relevant to policy decisions
affecting all of us, seem to have become corrupted?
The answer is not far to seek. It is an old proverb: “He who
pays the piper calls the tune.” One of the side-effects of ever-increasing
government control over education is ever-increasing control by the
establishment of funding for universities and their academics. This leads to
funds being directed where they will produce most benefit, not for the people
whom government is supposed to serve, but for the agendas of the vested
interests that are pulling the strings. It is not helped by billionaires, that
fund academic groups including key alarmist figures at places like Imperial
College.
The scientific establishment in the UK needs, and richly deserves,
a major shake-up. All the corrupted heads must roll.
Some good news from across the pond
For a change, there is some good news. It comes from the USA.
In May 2025, Donald Trump signed an executive order titled “Restoring Gold
Standard Science.” [[xi]].
Here are two quotes.
“My Administration is committed to restoring a gold standard
for science to ensure that federally funded research is transparent, rigorous,
and impactful, and that Federal decisions are informed by the most credible,
reliable, and impartial scientific evidence available. We must restore the American people’s faith
in the scientific enterprise and institutions that create and apply scientific
knowledge in service of the public good.
Reproducibility, rigor, and unbiased peer review must be maintained. This
order … ensures that agencies practice data transparency, acknowledge relevant
scientific uncertainties, are transparent about the assumptions and likelihood
of scenarios used, approach scientific findings objectively, and communicate
scientific data accurately.”
“For the purposes of this order, Gold Standard Science
means science conducted in a manner that is:
i.
reproducible;
ii.
transparent;
iii.
communicative of error and uncertainty;
iv.
collaborative and interdisciplinary;
v.
skeptical of its findings and assumptions;
vi.
structured for falsifiability of hypotheses;
vii.
subject to unbiased peer review;
viii.
accepting of negative results as positive
outcomes; and
ix.
without conflicts of interest.”
Amen!
[[iii]] https://reformpartygodalmingash.uk/a-brief-history-of-the-green-agenda-part-one-1968-to-1992-by-neil-lock/
[[iv]] https://reformpartygodalmingash.uk/a-brief-history-of-the-green-agenda-part-two-1993-to-2018-by-neil-lock/
[[v]] https://reformpartygodalmingash.uk/a-brief-history-of-the-green-agenda-part-three-2019-to-now/
[[vi]] https://www.drroyspencer.com/2026/01/tropical-tropospheric-temperature-trends-1979-2025-the-epic-climate-model-failure-continues/
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