We are told to “follow the science”. What we have witnessed far too often over the past four years is “follow the money”. Be it corrupt politicians, Big Pharma, medicos with vested interests, or globalist bodies seeking to control us all, we have seen how quickly and easily science and medicine can be corrupted and hijacked for nefarious ends.

Some of us were quite sceptical of what was being done to us in the name of Covid from very early on in the piece. Things did not seem to add up, and the hysterical media alarmism, coupled with Statist overkill via lockdowns and all the rest certainly made us wonder.

And many of us wondered out loud. We got absolutely hammered and hated on for daring to ask hard questions and query the official narrative. For simply expressing our concerns we were turned into despised pariahs and treated as the scum of the earth.

Yet increasingly we are being vindicated. Barely a day goes by when we do not learn even more about just how wrong so much of the “science” was, how dictatorial and totalitarian our governments were, and how much medical fascism was allowed to take place. Simply considering all the injuries and deaths so far with rushed and improperly tested medicines should wake us up.

All this, coupled with the clearly stated aims of individuals and groups like Schwab, Harari, Gates, the World Economic Forum and the Great Reset mob, makes it clear that we whistle-blowers and questioners were absolutely right to stick our necks out and dare to look closer at what was – and is – happening.

There are now innumerable articles and videos and plenty of books on all this. Last year I offered this list of titles.

Four recent articles that have appeared on these matters are worth drawing your attention to. The first one speaks about slodderwetenschap (the Dutch term for ‘sloppy science’). The authors say this especially occurred during the COVID-19 pandemic:

We had front-row seats to witness the media reporting claims of a breakthrough made one day, then dismissed the next. It’s one of the first occasions the public has been able to clearly see how messy the scientific process can be – when it’s done sloppily.

One of the more public facets in the swirling whirl of COVID-19 misinformation was the continuing role of Dr Anthony Fauci, the Chief Medical Officer to the President of the USA. Fauci insisted that his pronouncements of the moment, such as suggesting it would only take 15 days to slow the spread of the virus or that masks were ‘unnecessary’, were ‘science’ and as such not to be questioned. Yet, the main method of science is to question. Fauci was abusing his claims of expertise and in the process helping to erode the public’s trust in science itself.

The researchers argue that one of the drivers of sloppy science is that people find it hard to accept results that are a work in progress; they much prefer the neatness and superficial completeness that often comes with incorrect work. It can mean that shortcuts are taken – and alternatives are ignored because they cause disruption.

Results that are desired are often declared correct due to political and financial pressures or even fears. This culture involves accepting storylines that are presented without further examination (eg, Fauci’s ‘I am Science’). Naïve acceptance can cause real harm – especially when the initial claims need to be qualified or are disproved. What arose during the COVID-19 pandemic was the increasing proliferation of unsound science, which meant policy leaders – misled by misinformation – made terrible decisions with devastating ramifications. The debate about the longevity of lockdowns as a means of dealing with COVID-19 and the seemingly deliberate suppression of the role of natural immunity post-infection stand out as two prominent examples.

They close their piece by outlining seven critical mistakes where sloppy science can creep into the scientific process:

1. Jumping straight into giving explanations for unexpected observations. The impulse to be the first to obtain results makes shortcuts tempting.
2. Disregarding variables that could be of importance to the research. Selecting suitable variables is critical for good science – it’s a process that should not be rushed.
3. Not correctly considering the context of the experiment. For example, how the research relates to the real world.
4. Inflexible modelling. For example, only using a single model instead of an open-ended model to determine outcomes.
5. Making bad sampling assertions. Like applying statistical functions across populations as a whole – when they may only apply to specific subsets.
6. The overuse of labelling and categorisation.

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