Science in Society Archive

Use and Abuse of the Precautionary Principle

I-SIS submission to US Advisory Committee on International Economic Policy (ACIEP) Biotech. Working Group, July 13, 2000

The precautionary principle is accepted as the basis of the Cartegena Biosafety Protocol agreed in Montreal in January 2000, already signed by 68 nations who attended the Convention on Biological Diversity Conference in Nairobi in May, 2000. The principle is to be applied to all GMOs whether used as food or as seeds for environmental release.

The precautionary principle states that when there is reasonable suspicion of harm, lack of scientific certainty or consensus must not be used to postpone preventative action. There is indeed sufficient direct and indirect scientific evidence to suggest that GMOs are unsafe for use as food or for release into the environment. And that is why more than 300 scientists from 38 countries are demanding a moratorium on all releases of GMOs (World Scientists Statement and Open Letter to All Governments).

The precautionary principle is actually part and parcel of sound science. Science is an active knowledge system in which new discoveries are made almost every day. Scientific evidence is always incomplete and uncertain. The responsible use of scientific evidence, therefore, is to set precaution. This is all the more important for technologies, such as genetic engineering, which can neither be controlled nor be recalled.

Dr. Peter Saunders, Professor of Applied Mathematics at King's College London, co-Founder of ISIS, has written an article which shows how the precautionary principle is just codified common sense that people have accepted in courts of law and mathematicians have adopted in the proper use of statistics. It begins to clarify how scientific evidence is to be interpreted in a socially responsible way which is also in accord with sound science.
Key Words: scientific evidence, burden of proof, statistics, p values, law

Use and Abuse of the Precautionary Principle

Peter T. Saunders, Mathematics Department, King's College, London

There has been a lot written and said about the precautionary principle recently, much of it misleading. Some have stated that if the principle were applied it would put an end to technological advance. Others claim to be applying the principle when they are not. From all the confusion, it is easy to mistake it for some deep philosophical idea that is inordinately difficult to grasp (1).

In fact, the precautionary principle is very simple. All it actually amounts to is this: if one is embarking on something new, one should think very carefully about whether it is safe or not, and should not go ahead until reasonably convinced it is. It is just common sense.

Too many of those who fail to understand or to accept the precautionary principle are pushing forward with untested, inadequately researched technologies, and insisting that it is up to the rest of us to prove them dangerous before they can be stopped. The perpetrators also refuse to accept liability; so if the technologies turn out to be hazardous, as in many cases they have, someone else will have to pay the penalty

The precautionary principle hinges on concept of the burden of proof, which ordinary people have been expected to understand and accept in the law for many years. It is also the same reasoning that is used in most statistical testing. Indeed, as a lot of work in biology depends on statistics, misuse of the precautionary principle often rests on misunderstanding and abuse of statistics. Both the accepted practice in law and the proper use of statistics are in accord with the common-sensible idea that it is incumbent on those introducing a new technology to prove it safe, and not for the rest of us to prove it harmful.

The Burden of Proof

The precautionary principle states that if there are reasonable scientific grounds for believing that a new process or product may not be safe, it should not be introduced until we have convincing evidence that the risks are small and are outweighed by the benefits.

It can also be applied to existing technologies when new evidence appears suggesting that they are more dangerous than we had thought (as in the case of cigarettes, CFCs, greenhouse gasses and now GMOs). Then, it requires that we undertake research to better assess the risk and that in the meantime, we should not expand our use of the technology and should put in train measures to reduce our dependence on it. If the dangers are considered serious enough, then the principle may require us to withdraw the products or impose a ban or a moratorium on further use.

The principle does not, as some critics claim, require industry to provide absolute proof that something new is safe. That would be an impossible demand and would indeed stop technology dead in its tracks, but I do not know of anyone who is actually demanding it. The precautionary principle does not deal with absolute certainty. On the contrary, it is specifically intended for circumstances where there is no absolute certainty.

What the precautionary principle does is to put the burden of proof onto the innovator or perpetrator, but not in an unreasonable or impossible way. It is up to the perpetrator to demonstrate beyond reasonable doubt that it is safe, and not for the rest of society to prove that it is not.

No one should have any difficulty understanding that because precisely the same sort of argument is used in the criminal law. The prosecution and the defence are not equal in the courtroom. The members of the jury are not asked to decide whether they think it is more or less likely that the defendant has committed the crime he or she is charged with. Instead, the prosecution is supposed to prove beyond reasonable doubt that the defendant is guilty. Members of the jury do not have to be absolutely certain that the defendant is guilty before they convict, but they do have to be confident they are right.

There is a good reason for adopting a burden of proof that assumes innocence until proven guilty. The defendant may be guilty or not, and may be found guilty or not. If the defendant is guilty and convicted, justice has been done, as is the case if innocent and found not guilty. But suppose the jury reaches the wrong verdict, what then?

That depends on which of the two possible errors was made. If the defendant actually committed the crime, but found not guilty, then a crime goes unpunished. The other possibility is that the defendant is wrongly convicted of a crime, in which case an innocent life is ruined. Neither of these outcomes is satisfactory, but society has decided that the second is so much worse than the first that we should do as much as we reasonably can to avoid it. It is better, so the saying goes, that "a hundred guilty men should go free than that one innocent man be convicted". In any situation in which there is uncertainty, mistakes will be made. Our aim is to minimise the damage that results when mistakes are made.

Just as society does not require the defendant to prove innocence, so it should not require objectors to prove that a technology is harmful. It is for those who want to introduce something new to prove, not with certainty, but beyond reasonable doubt, that it is safe. Society balances the trial in favour of the defendant because we believe that convicting an innocent person is far worse than failing to convict someone who is guilty. In the same way, we should balance the decision on hazards and risks in favour of safety, especially in those cases where the damage, should it occur, is serious and irredeemable.

The objectors must bring forward evidence that stands up to scrutiny, but they do not have to prove that there are serious dangers. It is for the innovators to establish beyond reasonable doubt that what they are proposing is safe. The burden of proof is on them.

The Misuse of Statistics

You have an antique coin that you want to use for deciding who will go first at a game, but you are worried it might be biased in favour of heads. You toss it three times, and it comes down heads all three times. Naturally, that does not do anything to reassure you, until someone who claims to know something about statistics comes along, and informs you that as the "p-value" is 0.125, you have nothing to worry about. The coin is not biased.

Does that not sound like arrant nonsense? Surely if a coin comes down heads three times in a row, that cannot prove it is unbiased. No, of course it cannot. But this sort of reasoning is too often being used to prove that GM technology is safe.

The fallacy, and it is a fallacy, comes about either through a misunderstanding of statistics or a total neglect of the precautionary principle - or, more likely, both. In brief, people are claiming that they have proven that something is safe, when what they have actually done is to fail to prove that it is unsafe. It's the mathematical way of claiming that absence of evidence is the same as evidence of absence.

To see how this comes about, we have to appreciate the difference between biological and other kinds of scientific evidence. Most experiments in physics and chemistry are relatively clear cut. If you want to know what will happen if you mix, say, copper and sulphuric acid, you really only have to try it once. If you want to be sure, you will repeat the experiment, but you expect to get the same result, even to the amount of hydrogen that is produced from a given amount of copper and acid.

In biology, however, we are dealing with organisms which vary a lot and never behave in predictable, mechanical ways. If we spread fertiliser on a field, not every plant will increase in size by the same amount, and if you cross two lines of corn not all the resulting seeds will be the same. So we almost always have to use some statistical argument to tell us whether what we observe is merely due to chance or reflects some real effect.

The details of the argument will vary depending upon exactly what it is we want to establish, but the standard ones follow a similar pattern. Suppose, that plant breeders have come up with a new strain of maize, and we want to know if it gives a better yield than the old one. We plant each of them in a field, and in August, we harvest more from the new than from the old. That is encouraging, but it might simply be a chance fluctuation. After all, even if we had planted both fields with the old strain, we would not expect to have obtained exactly the same yield in both fields.

So what we do is the following. We suppose that the new strain is the same as the old one. (This is called the "null hypothesis", because we assume that nothing has changed.) We then work out the probability that the new strain would yield as well as it did simply on account of chance. We call this probability the "p-value". Clearly the smaller the p-value, the more likely it is that the new strain really is better - though we can never be absolutely certain. What counts as 'small' is arbitrary, but over the years, statisticians have adopted the convention that if the p-value is less than 5% we should reject the null hypothesis, i.e. we can infer that the new strain really is better. Another way of saying the same thing is that the difference in yields is 'significant'.

Note that the p-value is neither the probability that the new strain is better nor the probability that it is not. When we say that the increase is significant, what we are saying is that if the new strain were no better than the old, the probability of such a large increase happening by chance would be less than 5%. Consequently, we are willing to accept that the new strain is better.

Why have statisticians fastened on such a small value? Wouldn't it seem reasonable that if there is less than a 50-50 chance of such a large increase we should infer that the new strain is better, whereas if the chance is greater than 50-50 - in racing terms if it is "odds on" - then we should infer that it is not.

No, and the reason why not is simple: it's a question of the burden of proof. Remember that statistics is about taking decisions in the face of uncertainty. It is serious business recommending that a company changes the variety of seed it produces and that farmers should switch to planting the new one. There could be a lot of money to be lost if we are wrong. We want to be sure beyond reasonable doubt, and that's usually taken to mean a p-value of .05 or less.

Suppose that we obtain a p-value greater than .05, what then? We have failed to prove that the new strain is better. We have not, however, proved that it is no better, any more than by finding a defendant not guilty we have proved him innocent.

In the example of the antique coin coming up three heads in a row, the null hypothesis was that the coin was fair. If so, then the probability of a head on any one toss would be 1/2, so the probability of three in a row would be (1/2)3=0.125. This is greater than .05, so we cannot reject the null hypothesis, i.e. we cannot claim that our experiment has shown the coin to be biased. Up to that point, the reasoning was correct. Where it went wrong was in claiming that the experiment has shown the coin to be fair.

Yet that is precisely the sort of argument we see in scientific papers defending genetic engineering. A recent report, "Absence of toxicity of Bacillus thuringiensis pollen to black swallowtails under field conditions" (2) is claiming by its title to have shown that there is no harmful effect. Only in the discussion, however, do they state correctly that there is "no significant weight differences among larvae as a function of distance from the corn field or pollen level".

A second paper claims to show that transgenes in wheat are stably inherited. The evidence for that is the "transmission ratios were shown to be Mendelian in 8 out of 12 lines". In the accompanying table, however, six of the p-values are less that 0.5 and one of them is 0.1. That is not sufficient to prove that the genes are unstable, or inherited in a non-Mendelian way. But it certainly does not prove that they are, which is what is claimed.

The way to decide if the antique coin is biased is to toss it more times and record the outcome; and in the case of the safety and stability of GM crops, more and better experiments should be done.

The Anti-Precautionary Principle

The precautionary principle is such good common sense that one would expect it to be universally adopted. Naturally, there can be disagreement on how big a risk we are prepared to tolerate and on how great the benefits are likely to be, especially when those who stand to gain and those who will bear the costs if things go wrong are not the same. It is significant that the corporations are rejecting proposals that they should be held liable for any damage caused by the products of GM technology. They are demanding a one-way bet: they pocket any gains and someone else pays for any losses. It's also an indication of exactly how confident they are that the technology is really safe.

What is baffling is why our regulators have failed and continue to fail to act on the precautionary principle. They tend to rely instead on what we might call the anti-precautionary principle. When a new technology is being proposed, it must be permitted unless it can be shown beyond reasonable doubt that it is dangerous. The burden of proof is not on the innovator; it is on the rest of us.

The most enthusiastic supporter of the anti-precautionary principle is the World Trade Organisation (WTO), the international body whose task it is to prevent countries from setting up artificial barriers to trade. A country that wants to restrict or prohibit imports on grounds of safety has to provide definitive proof of hazard, or else be accused of erecting false barriers to free trade. A recent example is WTO's judgement that the EU ban on US growth-hormone injected beef is illegal.

Politicians should constantly be reminded of the effects of applying the anti-precautionary principle over the past fifty years, and consider their responsibility for allowing corporations to damage our health and the environment, which could have been prevented. I mention just a few: mad cow disease and new variant CJD, the tens of millions dead from cigarette smoking, intolerable levels of toxic and radioactive wastes in the environment that include hormone disrupters, carcinogens and mutagens.


There is nothing difficult or arcane about the precautionary principle. It is the same sort of reasoning that is used in the courts and in statistics. More than that, it is just common sense. If we have genuine doubts about whether something is safe, then we should not use it until we are convinced it is all right. And how convinced we have to be depends on how much we need it.

As far as GM crops are concerned, the situation is straightforward. The world is not short of food; where people are going hungry, it is because of poverty. There is both direct and indirect evidence to indicate that the technology may not be safe for health and biodiversity, while the benefits of GM agriculture remain illusory and hypothetical. We can easily afford a five-year moratorium to support further research on how to improve the safety of the technology, and into better methods of sustainable, organic farming, which do not have the same unknown and possibly serious risks.

Article first published 2000

Notes and references

  1. See, for example Holm & Harris (Nature 29 July, 1999).
  2. Wraight, A.R. et al, (2000). Proceedings of the National Academy of Sciences (early edition). Quite apart from the use of statistics, it generally requires considerable skill and experience to design and carry out an experiment that will be sufficiently informative. It is all too easy to fail to find something even when it is there. Our failure to observe it may simply reflect a poor experiment or insufficient data or both.
  3. Cannell, M.E. et al (1999). Theoretical and Applied Genetics 99 (1999) 772-784.

Got something to say about this page? Comment

Comment on this article

Comments may be published. All comments are moderated. Name and email details are required.

Email address:
Your comments:
Anti spam question:
How many legs on a tripod?