Quantitative Unease

A column dedicated to demystifying psychotherapy research – love it, hate it, or both…at least try to know what it’s all about!

In the Name of Science

Words are a derivative of instincts and form the bridge between intra and interpersonal psychological processes. They affect us at conscious and unconscious levels. The word ‘science’ has become popular. You hear it in the daily news, on the radio, and read about it in the papers. Governmental decisions are based on what “the science” tells us about ‘the virus’.  Or better, what it tells them, there is a sharp distinction between ‘them’ and ‘us’. They know, we don’t. If you still aren’t convinced that you should concern yourself with quantitative research and ask why you should think about the nomothetic paradigm and learn how to review research evidence, here is the reason: because it enables you to evaluate ‘their’ omnipotent claims. And you can discover what lies behind these two innocuously sounding words ‘the science’.

What exactly is science and what does ‘the’ science tell us? The word science is derived from the Latin word ‘scire’, meaning ‘to know’. There is something magical about knowing and knowledge, the translation of instincts from primary into secondary processes. Admittedly. Until you are fully analysed, then it becomes clearer. But doesn’t the word ‘science’ sound so much more mystical than knowledge? ‘Science’ captures our imagination and lures us back into the primary process. Thankfully, the ego, like language, is evolving. Knowledge becomes more sophisticated and talking about ‘the’ science is no longer adequate. It’s simply ‘science’. In the world of science, distinctions are made between ‘hard’ and ‘social’ sciences. ‘Hard’ science is based on a methodology in line with the nomothetic paradigm. Astrology is rejected as science, because the rules and procedures it uses to obtain knowledge are considered to be unscientific.

When ministers refer to what ‘the science tells us’, they really mean ‘scientific evidence’, without saying so. Typical. Politicians mean what they don’t say, and say what they don’t mean. But what counts as scientific evidence? For Aristotle, knowledge was related to observed facts. Later philosophers held that this knowledge must be based on what is ‘known’ and ‘true’. Now we’re entering an uncertain territory and find ourselves on a slippery slope. What is ‘truth’? Well, it depends on which epistemological and ontological stance you favour. Relativism, for example, assumes that there is no such thing as ‘the’ truth and reality is a projection of human imagination.

When politicians assert that ‘the science’ can provide them with ‘the’ truth about infection rates, transmission and treatment of the Sars-Cov-2 virus, they have embraced a positivist perspective. Positivism, often equated with empiricism, assumes that there is an objective reality. The truth can be derived at through observations and experiments.

Positivist assumptions are drawn on to offer ‘scientific’ solutions to problems. However, these solutions might not lead to the desired result in ‘the war’ against the virus, as politicians suggest. On the contrary, it might have the opposite effect. And the projected aggression and hatred into ‘the virus’, is indicative of an undigested death instinct. Experienced as coming from the outside instead of the inside, the death instinct is defended against by expulsion into the outside world. But projections come back with a vengeance when nobody identifies with them. Neither projections nor assumptions are therefore conducive to providing solutions. If politicians want to fight a ‘nuclear war’ and ‘defeat the virus’ with ‘frontline staff’, they should consult with the Ministry of Defence to develop a war strategy, instead of relying on positivism. Or join a group analytic group to understand the neurotic aggression of man and his war, which is driven by a fantasied threat and lack of conscience. Therapy might cure the contemporary war rhetoric, result in healthy scepticism about ‘facts’ and a recognition that reality might be socially constructed. It should at least be considered. However, if politicians reject social constructionism, then they should be precise and consistent, otherwise their ‘empirical’ stance is nothing but a farce.

I cringe when they make statements about the ‘effectiveness’ of immunisation against ‘the virus’. It’s ‘efficacy’, gentlemen. I’m not stuck in the anal stage when pointing out that ‘effectiveness’ and ‘efficacy’ aren’t the same. There is a huge difference. Medication is effective when it has been trialled on a large, representable number of people from diverse backgrounds. People like you and me, who get better. ‘Efficacy’ refers to outcomes of high quality randomised controlled trials, which are conducted under ‘ideal conditions’, comparable to a laboratory. Their effectiveness hasn’t been proven yet. Drugs must be trialled in the real world, not only on carefully selected samples, to warrant the term ‘effective’. When you listen more closely to the narrative about Covid-19, scientists speak of ‘efficacy’. Ministers don’t tend to do so. Driven by repetition compulsion, they make the same mistake again and again. In the media, ‘efficacy’ and ‘effectiveness’ are often used interchangeably. It’s wrong, scientifically speaking. Hence, claims about the effectiveness of new vaccines or treatments are unjustified when outcomes are derived from efficacy studies. We find such exaggerations everywhere.

To get an accurate picture of the presented ‘evidence’, it’s a good idea to analyse power. Not only from a group analytic but also research perspective. Considering the nonchalance attitude towards numbers, checking findings seems sensible. How? Do power calculations. They tell you how many participants are required to avoid Type I or Type II errors. Rejection of a true null hypothesis, or accepting the null hypothesis incorrectly when differences do exist, that is. Power calculations can be done by using a formula and normally should form part of the design phase of a study. When you trial drugs and test hypotheses, the sample size you need depends on power, which is the probability that you correctly reject the null hypothesis. It is typically set at 80% or 90%. Power increases with larger samples. The ‘confidence interval’ gets smaller. However, studies with hundreds of thousands of participants cost too much, take too long and are unethical, if the drug is ineffective or harmful. The problem with small samples is that you might have a wide confidence interval, meaning your results are not precise. Precision refers to the variation of whatever you are studying, for example, blood pressure. A small variation means that most of your participants have similar blood pressure, so your confidence interval is small. However, since you can’t do much about natural variation, you have to increase your sample size to get positive results. Positive means detecting a true, statistically significant difference between the control and treatment group.

Sometimes, studies are underpowered. When too few people have been recruited and you discover a positive effect, it’s likely that the estimate of the magnitude of that effect is exaggerated. The size of the sample changes with the size of the effect you’re trying to detect. Detecting a positive 0.2 percent difference would need more participants than a study aiming to detect a 25 percent difference. When reviewing papers, you can determine from the effect sizes and the number of participants, how many positive results could have plausibly been expected. If the numbers appear too high, look up the formula and do the maths, because numbers can lie, as we know.

Take death figures. Normally a taboo word in British society, death has found its way into daily communication. We’re informed that millions are going to die. “Death is a woman”, Beauvoir famously wrote. Not on this occasion. Death doesn’t discriminate, although some are more vulnerable than others, and anatomy isn’t destiny, in contrast to Freud’s assertion. But how do we know that millions are going to die? Considering that there are several forms of the coronavirus, which differ in severity from ‘no’ over ‘mild’ to ‘severe’ symptoms, this is a pertinent question. Praise be to the Lord, if you’re religious. You can leave the answer to destiny or its darker cousin fate (neurosis), view death as punishment for your sins, or as sacrifice. If you don’t have faith in religion, praise be to probability science, statisticians and algorithms. Geniuses and machines have come up with ways to predict the future. Supposedly. Calculations are being made of how many will die. And who is likely to die. All in the name of science.

Great, except, if it was that easy to foresee what is going to happen, why is it not possible to accurately predict who, and with what severity, will be infected? Initially, children and young people had been considered low risk. Later figures showed that this was not the case. Then those figures have been questioned. Confusing at best, misleading at worst, what are we supposed to believe? Should we abandon science and search for answers in religion? Better not. Looking at its inflammatory dynamic in the history of civilisation, too many have already been sacrificed at the altar of ‘truth’. In our quest for answers, we shouldn’t make an unethical pact with the pharma devil either, because Mephistopheles will come and ask us to pay. Consulting forensic statisticians to spot suspicious patterns in raw numbers might help, even though it could lead to a sobering awakening. Not from the dead though.

But it forces us to take a closer look at the ‘death statistics’, which are ‘calculated’ in England in a most peculiar manner. We don’t find ourselves in the realm of positivism any longer, even if such relation may be desired. Instead, we encounter relativism. Anybody, who had been infected once with the coronavirus at any point in time, is included in the numbers. With trepidation, I read through the document. Even if a healthy person dies in a car accident, his death will still be included in the statistics. Who has cheated death? And whatever happened to transparency and accuracy? Wilfully forgotten or repressed in the name of science? Recovery from the amnesic syndrome will be slow when we’re being bombarded with more and more numbers, day after day. On official websites and in newspapers, we also find infection rate statistics. Simple line or bar graphs, histograms and/or percentages. They’re presented in a format so that the lay public can understand what these numbers mean.

“1 in 14 Britons has already been infected with the coronavirus.”

Trepidation has turned into horror as I’m scanning through the article. What scientific evidence is given in support of this claim? “A study of 20,200 people” is cited, where “7.1 percent had been infected with Sars-Cov-2, the virus that causes Covid-19.” The problem isn’t only that we’re suddenly in the land of causation, where “Sars-Cov-2 causes Covid-19”, although Covid-19 is another name for the Sars-Cov-2 virus, unless the Lancet isn’t anymore what it used to be. You don’t have to be an analyst either to know that Britain’s population isn’t 20,200 but closer to 68 million. Perhaps we should just view these figures as sensationalism and dismiss them on those grounds, since journalists want to sell newspapers. Fair enough. But the government? What excuse can we make for ministers? That they are suffering from obsessive-compulsive disorder, a compulsion to lie? Perhaps we shouldn’t pathologize without proper assessment. But doesn’t creation of fear distract from other crucial political decisions? We could analyse group dynamics and processes, personality types and characters more deeply. It seems though that staying at the manifest content will do.

Politicians want to exert power. But without doing a power calculation. Official figures aren’t just indicative of bad science. They serve as justification for unjustifiable decisions. No ‘official’ website exists, which critiques the methodology sections of the academic papers that are used to derive at whatever conclusion the flavour of the week is. We find no adequate description of studies and findings, including their flaws, so that everybody can understand these. Instead, we’re fed secondary literature, interpretations of interpretations and more figures. This discourse doesn’t just induce mental diarrhoea, it has given science a bad name. Instead of discussions about validity, we’re confronted with a repetitive war rhetoric. Words offer an illusory certainty. “You will defeat coronavirus”, the pronoun is left out. A grammatical mistake, but in times of war, who pays attention to inaccuracies?

It does beg the question though, what are the ‘experts’ talking about? A “virus” or “disease”? Definitions are needed. Alas, none are included in the writing about COVID-19, where “we need to find out as much as we can about it.”

“Finding out” sounds scientific. Maybe I should not automatically dismiss a National Health Research website on the grounds that it confuses virus with disease, or should I? Am I being too pedantic when I expect writers of official websites to be precise? Viruses, diseases and pathogens, are they really all the same? I’m not attempting to launch a devastating critique of the science of the Covid-19 discourse. One that would be comparable to Eysenck’s critique of Freud or Popper’s of positivism. I leave this until my narcissism will be cured.

In the meantime, I stick to asking questions and do what the websites invite good citizens to do: “explore the topic”. Oblivious of the extent of exaggeration and inaccuracy when I started embarking on this odyssey, my ignorance quickly turned into anger when realising that Mephistopheles was already collecting his prize. On the government information website, it says “Covid-19 studies, suitable for everybody.” Of course, they aren’t.

The virus narrative shifts from an uncompelling sales rhetoric into blunt polemic. Trump allegedly admitted that “the virus will probably get worse before it gets better.” Sounds like psychotherapy, where we tell patients that their symptoms might get worse at the beginning of treatment. However, viruses aren’t clients. Viruses survive, mutate or die, but they ‘don’t get worse’.

Words. How do we make sense of them? “The prime minister said last week he hoped for a return to normality by Christmas. Experts giving evidence, saying it was important to be realistic that the virus would still be here“. Imagine experts giving evidence in court about the pandemic, stating that the virus is ‘here’. Now picture the cross examination: “A virus is neither here nor there, a virus lives within our bodies this fact sheet says. Isn’t that true?” Let’s leave the squirming expert in the courtroom and move to ‘normality’.

What is normality and whose normality is the minister referring to? Does normality mean the norm (average) or most people? For the less scientific-minded person, who doesn’t think about statistical means, normality is probably associated with how life used to be. But homeless beggars on the street, abused women in violent relationships and poor pensioners may not want to go back to the ‘good old days’. And they don’t care when scientists make unscientific statements either:

“Sir Jeremy, a member of Sage, the government advisory body, said this infection is not going away, even if we have a vaccine or very good treatments, humanity will still be living with this virus for decades to come.”

I’m perplexed. A virus does not live ‘with us’ but within our bodies. Comparable to an uninvited cohabitant, who destroys our cells. Infections are successfully or unsuccessfully treated, or spread. Nothing to do with ‘going away’.

Trepidation, horror and anger have turned into disbelief. ‘Mirror, mirror on the wall, what is the purpose of it all?’ Words and phrases, which make up sentences. Paragraphs, which are meant to persuade, but control. A discourse that sounds more like pharmaceutical ideology than science:

“The vaccine is unlikely to have a durable effect, we have more disease, and more vaccinations and more disease, the idea that we’re going to eliminate it that’s just not realistic.”

Where exactly do we find the evidence for this grim claim? Plausibility does not suffice. Particularly, when life-changing instructions are given in the name of science.

Susanne Vosmer
s.vosmer@gmail.com