4

Inverse Insights

While direct insight grasps the point, or sees the solution, or comes to know the reason, inverse insight apprehends that in some fashion the point is that there is no point, or that the solution is to deny a solution, or that the reason is that the rationality of the real admits distinctions and qualifications.¹

(1) Give the next number in these sequences:

(a) 5, 7, 4, 6, 5, 8, 6, 6, 5, ......

(b) 75, .82, .77, .80, .79, .74, .79, .......

(c) 98.1, 99, 98.7, 98.8, 98.3, 98.7, ......

(d) 5, 1/2, --198, 17, 1.1, ......

2. Name the following geometrical figures.

[124]

(3) What is the numerical relationship between the side of a square and its diagonal? Or work out the square root of 2.

(4) What keeps an arrow moving in the air even though there is nothing pushing it?

(5) Drop an elephant and a feather off a high building at the same time. Which hits the ground first? Why?

(6) Can you distinguish patterns in the distribution of the stars in the night sky?

(7) Do you believe the weather forecast for the following day? Does it give probabilities or certainties? Can you have accurate predictions of the path a tornado will take? If you have complete information can you make that prediction?

1. The Experience of Inverse Insight

Have you ever had the experience of reading a rather difficult book, and despite your best efforts and concentration you are not getting the point? You presume that this author is very bright, and it is because of your lack of intelligence that you do not understand. You may give up there and then, and the author will always remain in your mind revered for his great intelligence and superior knowledge. You may also have the experience of persevering with great determination to get what he is saying and slowly and painfully discovering that the fellow is utterly confused, presents his material badly, and is trying to impress with big words and obscure ideas: he does not know what he is talking about. You have finally understood the author and the book; however, it is not an understanding of what is there rather, on the contrary, of what should be there, and is not. You are not grasping the message the author is trying to communicate but rather that he has no message, is quite confused, wrong, uttering nonsense. This is an insight; but it is a new, strange kind of insight.

Or perhaps you have had the experience of being asked to take the minutes at an important meeting. Unfortunately, the chairperson is not very competent and exercises no control over the proceedings. [125] So you listen to a stream of suggestions, pronouncements, interjections, retorts, declarations, digressions, appeals, defamation, arguments, winding their crooked way through the morning, until it is time for lunch and the meeting is mercifully called to a halt. What do you write in the minutes? There was no order or pattern, beginning or end, form or meaning to the whole procedure. Can you describe pure chaos? You can either give an exhaustive account of everything everybody said; or else report truthfully that it was a most chaotic meeting and that you cannot give an accurate account of chaos; or you can impose your own kind of order or pattern on the chaos to rescue what you think might be useful. An inexperienced secretary may blame him/herself for having difficulty following the discussion but the real difficulty lies not in being unable to follow what is going on, but in grasping that what is going on is pure confusion, chaos, a random stream of words with no meaning or pattern. Again, it is an insight; but of a different species from those we have met heretofore.

Or perhaps you are a bit of a gambler as well as a budding mathematician; so you try to construct a system of betting that will beat the roulette wheel, without cheating of course. How do you predict a random stream of numbers? How do you create a system that will beat the odds? You may notice that a certain number has not come up at all for a long time. Because you are a mathematician you know that occurrences tend to average out. If the number is falling behind its average, you might think that it has to catch up and so is now more likely to occur. But, do previous occurrences in a sequence like this change the probabilities of the next spin? When and if you grasp that they do not (hopefully before you become penniless), you are on the way to an inverse insight. The intelligibility of such a sequence of numbers is different from the direct intelligibilities which we have been considering up to now. It is with some shock and annoyance that we learn to accept certain limitations to direct understanding and how data are to be brought under law. There is no system that can be applied to a random stream of numbers which will enable you to beat the odds; whether you are trying to win or lose makes no difference; in the long run you lose. [126]

Sometimes it is assumed that the only real kind of knowledge is of certain, necessary, permanent truths. In these examples we begin to discover that most of our understanding of the material world yields not certainties but varying degrees of probability. The expectation of complete certainty is unrealistic in most areas of science; there is a convergence towards certainty but neither in classical method nor in statistical method do the conclusions of empirical science reach complete certainty. However, knowledge of probabilities is genuine human knowing - in most areas it is all that we can expect. That is the kind of universe we are living in; that is the kind of mind that we have.

In this chapter we explore these degrees or kinds of intelligibility in our universe. Not every area of data is susceptible to the kind of direct understanding that we have been considering up to now. We will identify classical method as the appropriate way of understanding data which are systematic and orderly. We will identify statistical method as appropriate for data which are a combination of systematic and nonsystematic. Finally, we will consider the empirical residue which lacks any immanent intelligibility but can be grasped indirectly by way of an inverse insight.

Our focus continues to be self-appropriation. We present many examples in the preliminary exercises and in the text; the purpose is to help you to recognize this experience as it happens in your own mind. If you find that the examples are not appropriate, substitute others familiar to you in your own discipline or field of competence, at your own level.

2. Inverse Insight Defined

An inverse insight is an insight; therefore, there will be found the five characteristics that we have already identified in the direct insight. There is a problem posed to intelligence from the experience of certain data; a question arises creating tension towards a solution. This is followed by a sudden enlightenment, which depends on inner conditions rather than on outer circumstances. There is a pivoting between the abstract and the concrete, and the solution passes into the habitual texture of the mind. The experience of discovering that an author or a lecturer has got it all wrong is a very liberating experience. But it starts with an expectation of a different kind of intelligibility. The tension is towards that kind of positive intelligibility which you have a right to expect of an author or professor. But you reach a block; you try everything; look at the data from all sorts of different points of view; you try to make sense of the confusion; come back to it again and again. Then, slowly it dawns on you. The fellow does not know what he is talking about; there is a stream of impressive words but no sense; all sound and fury signifying nothing. You will never be the same again. Once you have realized the emptiness of one author or philosopher or professor, you are constantly aware that there may be more of them out there.

An inverse insight is different from a direct insight; instead of grasping what is there, we grasp what is not there. We can define an inverse insight as an insight into the absence of an expected intelligibility. There are three characteristics of an inverse insight.² One, there are positive data. Two, there is a spontaneous expectation of a direct intelligibility. Three, the insight is into the absence of the expected intelligibility.

Firstly, there are positive data: there is something to be understood. All the above examples presume some data presented by the senses, memory or imagination, which poses a problem for understanding. An inverse insight is not an insight into a simple absence of data. It is not simply a correction of a previous mistake. There are positive data, but they do not seem to respond to the usual procedures. The book, which is full of nonsense, presents a multitude of data on every page. The unfortunate secretary has pages of words to deal with. The gambler can collect sequences of actual numbers for as long as he wishes. The data are there, but are they intelligible?

Secondly, it runs in the face of the spontaneous expectations of intelligence. We can only have insights if we ask questions; if there are no questions there are no insights. We can only ask questions if we expect and anticipate an answer. We usually expect to find a law, a regularity, a system, an explanation as the answer. The answer we [128] expect to be directly intelligible, otherwise it would not be an answer to the question. But in the inverse insight we get, not a direct intelligibility, but precisely a lack of the expected intelligibility.

Thirdly, the insight is into a lack, an absence, a deficiency in the expected intelligibility. We expect to find the rational, the systematic, the significant, the regular; but discover that we also must cope with the irrational, the nonsystematic, the insignificant, the irregular. The Greeks were puzzled by the relationship between the side of a square and the diagonal. Surely, such a basic relationship in the most regular of all geometrical figures could be expressed in rational whole numbers. But they found that it could not. They called it incommensurable; modern mathematicians call it irrational. If the side of a square has the numerical value of one, you expect the diagonal to be some rational number. But if you apply the theorem of Pythagoras you find that it is the square root of two. If you follow the rules for finding the square root of two, you find that it goes on and on; you keep expecting the further decimal places to reveal a pattern; but even the most powerful of modern computers has not found a pattern in this sequence of numbers. How strange! And so we begin to grasp that there are varying types, degrees and levels of intelligibility.

Often, the inverse insight is the realization that we have been asking the wrong question. Misguided questions point us in the wrong direction, we are barking up the wrong tree, seeking for something that is not to be found. The insight is into the mistaken anticipations of the question; we have to back up and start again. Aristotle, for example, was asking the wrong question about local motion. He saw that everything in his experience of everyday life comes to rest; when you stop pushing, it stops moving. Consequently, he assumed that rest was the natural state. Thus, he concluded that it is motion which needs to be explained: if something is moving there must be something or someone pushing. But what is pushing the heavenly bodies? What is pushing the arrow which is already in flight? This way of formulating the question inclined thinkers to invent theories of impetus, heavenly Movers, and other occult forces. This was an enormous block to progress in astronomy. Copernicus could obviously not be right; the earth could [129] not be moving because of the enormous force that would be needed to push it. It was only in the time of Galileo that the question was turned on its head. What needs to be explained is not motion, or rest, but changes in motion or rest. Galileo understood this and it was later formulated in the first of Newton's laws of motion, the law of inertia.

Expectations are conditioned by our education, culture, specialization, and degree of differentiation of consciousness. Expectations can be refined by the process of education but one normally approaches an area with an expectation of a positive, direct intelligibility. Inverse insights confer a limited grasp of the irregular, and the nonsystematic. The frustration of accepting an inverse insight as the limit of what can be reached often leads to a denial that this is a genuine insight. Until this century it was assumed that knowledge of probabilities is not real knowledge. Classical science had no place for such a deficient insight and presumed that everything could be understood by a totality of direct insights.

Intelligibility is the content of a direct insight. Therefore a direct insight will always grasp what is intelligible, significant, regular, systematic, relevant, meaningful. The inverse insight confronts us with the data that are not fully intelligible, are lacking significance, regularity, or relevance. But you cannot by definition grasp what is unintelligible or nonsystematic by way of a direct insight. The inverse insight confronts us with this reality of lack of intelligibility. We can only deal with the nonsystematic and unintelligible by the roundabout route of the inverse insight. Inverse insights are important because they reveal the degrees and kinds of intelligibility attainable in our universe. They reveal that there are degrees of intelligibility and that our universe is only to be understood correctly by a combination of direct and inverse insights. We will explore this in detail in our consideration of classical method, of statistical method, and of the empirical residue.

3. Classical Method

We consider classical method here because we wish to contrast it with statistical method, which follows immediately. The kinds of [130] direct insights we considered in chapters two and three were for the most part belonging to classical method. We use the word ‘classical’ to associate this type of insight with the scientists from Copernicus to Einstein: the historical period called the Scientific Revolution. Generally speaking, these scientists were all looking for the kind of direct insight which we have previously defined, and they in no way recognized the existence of inverse insights or statistical methods. We are appropriating their grasp of classical scientific method, but we disassociate ourselves from the philosophical assumptions which often accompanied their science.

3.1 Classical method described

Let us first of all describe a typical case of classical method at work and pick out the characteristic stages in the unfolding of this kind of direct insight. Let us dwell on the example of Galileo, as he set out to discover the nature of a free fall.

1. Heuristic anticipation. There was something to be understood; there is a wealth of data on falling bodies; it is a common experience of mankind; it is part of our everyday living; there is a something there which needs to be explained and so Galileo asked himself, 'What is the nature of a free fall?' He gave the unknown a name; he presumed there was an immanent intelligibility to be found.

2. Ordinary description. It is easy to give a description of the matter: heavy bodies fall, very light bodies seem to rise. Heavy bodies seem to fall faster and faster. The problem is set by commonsense observations and questions. You do not have to be a scientist to observe and describe instances of falling bodies and to ask why do they fall in such a way.

3. Similars are similarly understood. You start with instances which are similar from the point of view of common sense or of description. But you are moving towards a similarity which will be based on explanation. Galileo expected that similar instances of falling bodies would be explained in the same way. He did not expect to have one theory for Italy and another theory for Spain; one theory for gold and another theory for iron. There is something [131] behind all the instances of falling that is to be understood as common to them all.

4. Functional Relations. The Scientific revolution had discovered the importance of mathematics and how the regularities of nature could often be expressed in mathematical language. What were the variables which could be systematically related in order to formulate the regularity of a falling body? Galileo might have considered weight as significant; for Aristotle this was a determining factor. Distance is a factor because the farther an object falls, the faster it moves. Time is a factor because the longer the time, the faster it falls. Changes of velocity are a factor because you are talking of faster and faster. Acceleration is a key because that is the precise mathematical term for faster and faster. But what is the precise mathematical formula? How can the individual variables be related in a single function?

5. Scientific Description. This is the time for measurement, observation and experimentation; for rolling steel balls down an inclined plane and measuring time and distance as accurately as possible. Description becomes more and more precise depending on the materials, the instruments used, the care taken. Galileo's first discovery - actually from using a pendulum - was that weight made no difference. No matter what weight the balls or what material they were made of, the time and the distance did not change. This in itself was a major discovery and proved that Aristotle was wrong: the commonsense assumption that weight determines how fast bodies will fall was simply wrong. But, leaving aside weight, what was the relation between time and distance? He fixed the distance and measured the time taken; he repeated this a number of times for accuracy. He increased the distance and got another set of results. He decreased the distance and recorded further sets of figures.

6. Range of Possibilities. Now he put down his results on paper in the form of a table, with distances at the top and corresponding times at the bottom. How could these be related? Sequences of numbers can be generated by a variety of mathematical formulae. So Galileo looked at these series of tables struggling to find the formula that would unlock the secret. His familiarity with mathematics made a wide range of possibilities open to him. We might only think of [132] addition and subtraction; but there are also multiplication and division, roots and cubes, direct and inverse relations, etc.

7. Practical Techniques. Perhaps, he tried to represent the tables on a graph. It was easy to see that the more the time increased the more the speed increased. Just as in algebra you manipulate equations to find the value of the unknown, so Galileo explored possible relationships to find one that fitted.

8. Upper and Lower Blade. There was a process from below upwards, by which the data are gathered, selected, measured and begin to suggest possibilities; there was a movement from above downwards, by which he constructed various hypotheses, different formulae or functions, and found them wanting. There is a scissors-like movement from data to hypothesis, from hypothesis back to the data.

9. Insight. Suddenly and unexpectedly the insight came. It was a leap of constructive intelligence. He found a formula for the relationship between distance and time. 'Spaces traversed by freely falling bodies are proportional to the square of the times.' He had found a universal, abstract, functional relationship, which underlies all instances of all falling bodies. Any discrepancies could be explained by the deficiencies of his material or the inaccuracy of the measurements. Of course, he had to make the proviso of other things being equal; the figures did not match exactly. The law is true in a vacuum, but may not be true if friction was allowed to interfere; so he had to postulate a vacuum. Now that he understood acceleration, he could go on to work on measuring friction, projectiles, etc.

10. Verification. Undoubtedly he went back to check, perhaps constructing further experiments and tables, perhaps working further on the mathematics to verify again the correctness of the formula. Once he had this he could predict how falling bodies should behave in varying circumstances. Now, he could start studying projectiles because he had pinned down mathematically one of the major forces determining a trajectory.

Galileo succeeded in specifying the nature of a free fall in terms of simple mathematics, a simple rule, an abstract mathematical correlation of time, distance and velocity: distance is proportionate [133] to time squared. The abstract formula could be applied to any instance of a falling body and, provided that there were no outside interference, could be verified. It was a most satisfying kind of direct insight because it was so simple and explained so much. It applied to all instances of all falling bodies in all places. No wonder the scientists considered that mathematics was the key to understanding nature. They had uncovered a very basic regularity in the workings of nature.

3.2 Classical method defined

We could define classical method as, "the intelligent anticipation of the systematic-and-abstract on which the concrete converges." ³ When we were discussing the characteristics of direct insight we noted the constant pivoting between the abstract and the concrete, the intelligible and the sensible. The abstract is usually universal, functional, explanatory; expressed in concepts. The concrete is the data, the sensible presentations, the images, what is given in sensation. But these are not two separate things; it is precisely the intelligible in the sensible that we are trying to understand. Classical method seeks the systematic laws and functions immanent and verified in the data.

The key word of the definition is convergence: the concrete converges on, comes closer to, the abstract. There can never be a perfect coincidence between the concrete and the abstract. There are always inaccuracies because of impurity of samples, limited power of instruments, limitations on measurement, and the impossibility of excluding all extraneous influences. But the point that Galileo probably noted was that the more accurate his measurements the closer his figures converged on the abstract law. The more he excluded friction, the finer the materials he used, the closer his figures converged on the abstract mathematical formula.

Classical laws have to postulate 'other things being equal.' They have to presume that extraneous influences have been excluded. Chemists go to great lengths to procure pure samples, to have clean implements, to control all factors of pressure, temperature, etc. But this can never be complete or perfect; it is just sufficient for practical purposes. You have to make the assumption that the imperfections [134] of the sample or instruments are insignificant. It is impossible to exclude all extraneous influences in principle. There is always room for experimental error.

As a matter of principle there can never be a complete coincidence between the abstract law and the concrete data. The abstract will always be abstract; the concrete, concrete. The intelligible will be intelligible, the sensible sensible. The definition of a circle will always be abstract; any concrete circle will always be imperfect. If it were perfect you would not be able to see it and it wouldn't be a concrete; it is imperfect because it is a concrete realization of the definition of a circle, an image that converges on the concept. But the image can never coincide with the idea because they are different kinds of things; the image is a picture; the concept is an intelligible relation.

3.3 Misunderstanding

The kind of classical insight exemplified by Galileo is extremely satisfying: a single neat formula reveals the regularity underlying a vast multitude of data. When related laws are also expressed with similar simplicity, a system of laws can be set predicting and controlling a vast range of data as in the planetary system. The protagonists of the Scientific Revolution built up these systems of laws along the classical lines. Newton was, perhaps, the most successful of all these thinkers, putting together a synthesis of physical laws concerning motion in a highly systematic way.

Further, the application of these laws to the concrete proved to be very successful. In the technology of war, ballistics, projectiles, battering rams, gunpowder, firearms, began to change the balance of power. Applications in navigation, manufacture, machines, the steam engine, electricity, etc. began to transform the way we lived. The ideal of complete control over the workings of nature seemed to be within their grasp. No wonder the nineteenth century was the age of optimism.

However, a basic misunderstanding of the scope of classical laws lay at the root of all this as would become very evident in the twentieth century. It was the assumption that classical laws are the [135] only kind of scientific laws. Coupled with that was the assumption that once all these laws were known, that knowledge would confer complete control over nature, and the possibility of predicting and controlling anything that could happen.

Laplace is said to have maintained this position in its purest and most explicit form.⁴ He claimed that when all the laws were understood, and we knew one situation in world process, we could work forward to predict accurately any subsequent situation. This was a position of complete determinism; it did not leave room for freedom or chance or probability. The universe was a vast mechanical machine obeying the fixed classical laws of physics. This position assumed that all the data of world process were systematic and so could be understood by laws of the classical type. Just as it is relatively easy to predict an eclipse because our solar system is systematic, so Laplace held that all data are systematic and that it was only a matter of time before finding the other laws that would make total prediction and control possible. He contended that the totality of classical laws would completely explain all the data of experience and enable exact predictions and control to be exercised on everything. There are many things one could say in answer to this, but let us concentrate on the implied misunderstanding of the scope of classical laws.

Classical laws express what would happen if certain conditions were fulfilled. But how do we determine whether the conditions are fulfilled? Galileo's law of falling bodies will only work perfectly in a vacuum. But there is no perfect vacuum, so does that mean that it cannot be verified? No! The nearer the situation comes to a perfect vacuum, the more accurate the results become. It is statistical laws which will determine when, where, and how the conditions will be fulfilled. Classical laws can only be verified given certain conditions. But classical laws do not determine those conditions.

Classical laws assume 'everything else being equal.' But how do we determine that? Is everything equal when you throw a feather and an elephant off a high building? Classical laws work if no extraneous influences interfere. But classical laws cannot guarantee the exclusion of such interference. A chemistry professor always faces the disquieting possibility that his experiment may not work. [136]

The idea of having full information on any situation is an illusion. Laplace argued that if you have full information on one situation, you could predict any situation. But full information would be all the relevant facts. What are the relevant facts by which you could predict the trajectory of a falling leaf? Immediate conditions of height, wind, warmth, pressure, humidity, etc. are obviously relevant. But each of these variables is conditioned by a whole series of diverging variables. There is no end to the quest for full information. To have all the relevant information you would have to know everything about everything and that is beyond the human mind.

Classical laws are abstract and when applied to the concrete they explain certain aspects of the data but they do not explain all the data. Some data are amenable to explanations of the classical type and so are systematic or regular; other data cannot be brought under such laws and so are relatively nonsystematic or irregular. As well as the regularity of the solar system, there is also the relative irregularity of the weather. As well as what can be subsumed under laws, there is always the residue of data pertaining to particular places and times which can never be fully explained - as we shall see. Between the abstract law and the concrete instance, there is always needed an insight into which laws apply in what order of precedence; there is always this 'gap' to be filled by a further insight.

History provided its own answer to Laplace. These optimistic assumptions of automatic progress in science, technology, economics, medicine, etc. were brought down to earth by the first world war, the great depression and the discovery by scientists themselves of the need for a statistical method. It seems to have been in the field of nuclear physics that the principle of indeterminacy was most clearly identified and it was realized that statistical techniques were needed to deal with questions about the occurrence of particular states and events. Use of statistical methods spread to all other sciences through the century and was found to be successful. Einstein was apparently the last of the pure classical scientists, insisting to the end that ‘God does not play dice with the world.’ [137]

4. Statistical Method

4.1 Statistical Method Described

Statistical method is analogous to classical method, that is, it shares some common characteristics. However, it anticipates a different kind of intelligibility. To begin we will describe a typical case and illustrate the unfolding of statistical method in ten steps, which parallel those of classical method. Let us assume that we are investigating whether there is a connection between smoking and lung cancer. Is there a significant correlation between cases of smokers and those with lung cancer?

1. Heuristic Anticipation. As classical method anticipates an understanding of 'the nature of' something, so statistical method anticipates understanding 'the state of.' The state that is to be defined is the distribution of incidents of lung cancer compared to incidents of smoking. We are wondering whether the correlation will be significant or random.

2. Ordinary Description. At first it is only a suspicion; someone makes a connection and begins to wonder; the public are alerted. An interest group is formed. They point to this and that example, but it is rather hit and miss. Figures that are limited, unreliable, approximate and ambiguous are produced. They may be slanted if they are produced by an interest group.

3. Similars are similarly understood. We start from sensible similarities, from description. A neutral group is set up to start a pilot project. Describe the kinds of smoking and the possible connection with lung cancer. Description is too vague and ambiguous; the similarities of description have to shift to the similarities of explanation.

4. Functional relations. Terms need to be defined. Compare sets of classes of events with sets of probabilities as an ideal. What would be the normal distribution of incidents of lung cancer if it were random? There has to be some basis of comparison to judge whether the results are significant. [138]

5. Scientific Description. The data have to be collected. Everything has to be done accurately, precisely, objectively. Normally the whole population will not be investigated; a random sample will be chosen, but it must be sufficiently large to be representative of the whole population. Perhaps, certain controls can be included for comparison. This will involve training personnel, making questionnaires, collecting names and addresses, doing interviews, coding information, etc.

6. Range of Possibilities. Meanwhile someone has to work out what deviation from the norm would be significant. If a sample has been used, then the smaller the sample the less convincing the results will be. What random deviation from the norm can be expected? What is the norm?

7. Practical Techniques. There are various ways of coding information so that correlations can be easily made for age, sex, smoking, race, religion, anything that might be significant. There are statistical techniques for establishing probabilities, an average, and significant deviation.

8. Upper and Lower Blade. There is both a movement from the data to hypothesis, and from hypothesis to the data, as in the scissors movement of a heuristic. The figures represented on a diagram will begin to suggest patterns. But the mathematics of statistics, size of sample in relation to population, etc. will suggest which correlations might be significant.

9. Insight. Just as in classical method, so also in statistical method there is a moment of insight, when all the work is summarized on tables and diagrams and the relationship is seen to be significant or simply random. The conclusion can be stated in a proposition that is universal and abstract, a statement of a probability function or a statistical law.

10. Verification. There should be a process of verification, a checking of the mathematics, a review of the procedures used in collecting data, a crosscheck on extraneous factors which might have interfered, a comparison with control groups or factors which were included for this purpose. [139]

This is an outline of the use of statistical procedures in one particular area. In fact, these procedures are becoming more and more common and accepted in most areas of scientific work. Statistical method does parallel classical method but is anticipating only a statement of a statistical probability rather than a universal law applicable to all cases. It is this aspect of probability which made it difficult for scientists to accept it as scientific knowing. But it has been quite successful in its own way, and barriers to its acceptance have broken down. More and more we are accepting the idea of probability, of averages, means, frequencies, rates, and using these in our understanding of our universe. Let us examine more closely the notion of probability and the definition of statistical method.

4.2 Statistical Method Defined

Let us be clear about a few preliminary definitions:

An event is the occurrence of a defined variable such as an incidence of leukemia, a death, a birth, an accident, etc. An event is the answer to the question, did this occur? A frequency is the number of events so defined in terms of how much, for so many: how many deaths, for how many of the population, over a certain period of time? A frequency can be either ideal or actual. It is ideal if it is a theoretical statement of an abstract statistical correlation. It is easy to see that the probability of a toss of a coin producing a certain result is fifty-fifty; that is the ideal. But if you toss a coin for a certain number of times, keeping an account of the results, you get an actual or real frequency.

We can define statistical method as, "intelligent anticipation of the systematic-and-abstract setting a boundary or norm from which the concrete cannot systematically diverge." ⁵ Statistical method yields laws that are systematic and abstract. The laws of probability of occurrence of events in states are abstract and systematic. Death rates, average life expectancy, mean temperature, incidents of diseases, frequency of accidents, can all be stated in laws that apply to a given population at a certain time and place.

In classical law we saw that the key word was convergence; the concrete converges on the abstract. In statistical method the key [140] term is ‘nonsystematic divergence.’ You do not expect actual frequencies to coincide with ideal frequencies; you do not expect the tossing of a coin to conform always to the average of fifty-fifty. You expect a divergence. Just because the life expectancy is fifty years, it does not mean that everyone dies at fifty years of age. There will be fluctuations, ups and downs, divergences.

But the crucial point is that these divergences cannot be systematic. If they were systematic, they could be explained by the use of classical method, which specializes in dealing with the systematic. If there were a systematic divergence it would indicate that the averages were wrong: some other factor is operating. If you find that a certain number is recurring on a roulette wheel, then you begin to suspect cheating. If the actual figures for deaths are always above expectations, then suspect some new disease interfering. The actual frequencies fluctuate around the ideal but they do so in a way that is nonsystematic, cannot be explained, is not subject to law.

Chance can be defined as the random divergence of the actual from the ideal. In the tossing of a coin the ideal frequency will be fifty-fifty. But if you actually toss a coin you find strings of heads and tails. There is a divergence of the actual from the ideal. It is that divergence which constitutes chance. The ideal remains the same; but there is a divergence which is nonsystematic; the divergence cannot be controlled or predicted. The actual fluctuates around the ideal nonsystematically. That is where luck comes in; that is the aspect of coincidence.

Statistics is true knowledge. It does give a limited intelligibility; it is not a mere cloak for ignorance. The scientists of the classical mold had an ungrounded expectation of a much more complete intelligibility than that provided by statistics. They hoped that the time would come where everything could be explained by classical laws. Then statistics would not be needed. But the importance and success of statistical method in so many areas of science today belies this claim.

The insight of statistical method can be called a devaluated inverse insight. It is an inverse insight because it is into a lack of expected intelligibility. But it is not a pure inverse insight because there is an intelligibility that is grasped in the probabilities and [141] averages and frequencies that are expressed in statistical laws. It is a sort of in-between case. It reflects the fact that there are degrees of intelligibility and at least two complementary ways of understanding data.

It is much easier and more satisfying to understand systematic processes, because a cluster of insights grasps an interrelated set of intelligibilities. One cluster of insights orders all the data about the movements of the planets for decades to come; accurate predictions can be made for centuries ahead. But to understand the weather pattern, here, tomorrow, you need comprehensive data and numerous insights, yet in the end can do no more than state a probability. The same process of data collection and understanding have to be repeated each day to give a probable forecast for the following day. In statistical method long-term prediction is extremely difficult; situations cannot be deduced from one another; each situation is open to extraneous forces; statistical laws change over time and place.

There are four characteristics of statistical method that set it off from classical method and they are worth considering:

5. Complementarity of Classical and Statistical Investigations

We have considered classical and statistical methods separately to show that they are distinct empirical methods which are an accepted part of contemporary science. In conclusion, we wish to show how these methods are not mutually exclusive, but complementary to one another in many ways. It is not that physics is a classical science to the exclusion of statistical method; or that the human sciences are statistical to the exclusion of classical. There is an overlapping and a complementarity that has to be explored.

1. Complementarity in Heuristic Anticipations. Classical method anticipates an understanding of the systematic. Statistical method anticipates an understanding of the nonsystematic. Data will be either systematic or nonsystematic or any combination of the two. The same data are considered from the point of view of the systematic and are understood in classical laws; under a different aspect they are nonsystematic and are studied by statistical method. All data will be either systematic or nonsystematic; hence all data will be covered by a combination of classical and statistical methods. (In chapter nine we will consider dialectical method and in chapter eleven a brief mention of genetic method.)To understand a single traffic accident you must invoke both classical laws and statistical laws. Classical laws state that if a car is driven at a certain speed around a corner of a given camber, it will roll; that without oil the engine will seize; that braking can reduce speed at a given rate for each vehicle without skidding. But why is a particular driver going around a corner too fast? Why was there no oil in the engine? Why did the driver fail to stop in time? Was the driver drunk, tired, inattentive, or incompetent? The coincidence of these factors is governed by statistical method.

2. Complementarity in procedures. We have already shown the parallel procedures of classical and statistical methods in the examples of Galileo and the statistical relation between smoking and lung cancer. These procedures are complementary in that the isolation of the systematic prepares the way for the determination of the nonsystematic. Similarly, the isolation of the nonsystematic prepares the way for a determination of [144] the systematic.

Galileo used mathematics as the heuristic tool in his search for the law of falling. To do so he had to try to exclude extraneous influences. He made his equipment as perfect as possible, and his measurements of time and distance as accurate as possible. Given the limits of his instruments he could not produce a vacuum or perfect conditions so he had to try to eliminate possible experimental error by repeating his measurements and experiments to average out the discrepancies. When he had formulated his classical explanation for the law of falling bodies, he was in a position to study friction. He had determined how bodies should behave in a vacuum; by measuring the discrepancy in the figures he would be able to study the different interferences, especially friction. He would be able to measure the degrees of friction on an elephant and a feather dropped from a height and thus to explain why the elephant would fall faster.

We used the example of the distribution of incidents of lung cancer in relation to cigarette smoking to illustrate statistical method. But if this study were to indicate that there is a significant positive correlation between the two variables, then that would be a hint to the classical investigator that there was a is the causal relation between lung cancer and smoking. What element of smoking could be the causative factor; what is the precise element of smoke that causes cancer; is it a causative element or merely a catalyst, etc.

Mendel studied genetics using a statistical method.⁶ He noted the recurrence of traits in a series of experiments with peas and was able to formulate statistical laws as to the probability of certain traits repeating themselves after a certain number of generations. That was the hint the biologists needed to go in search for the reason for this recurrence, and led to the discovery of genes, chromosomes and D.N.A.

3. Complementarity in Formulation. Classical formulations regard correlations, which are verified only in events. Statistical formulations regard events, which are defined only by correlations. [145]

Classical laws presume that no extraneous influences interfere. They make the proviso of 'other things being equal'. The law of the lever states what would happen, if you apply a lever with a given force and a fixed fulcrum to a certain body. But that does not tell you how it might actually be used to move the earth. What material could it be made of? What happens in theory in a diagram may not be possible in reality because of the material that would be needed. Classical laws need statistical laws in order to be applied in the concrete. A physics professor might set up an experiment to demonstrate some classical law, but much to his embarrassment the experiment does not work; he cannot completely exclude all extraneous factors.

On the other hand, statistical correlations only possess scientific significance if the events are defined by the correlations of classical laws. An event is 'what' it is that occurs; it is the definition of this 'what' which makes statistical method possible. In doing the survey on smoking and lung cancer, it was vital to define the terms clearly; what kind of cancer; what kind of cigarette smoke; etc. If doctors were confusing lung cancer with tuberculosis or bronchitis, the results would be useless. If the smokers included those who did not inhale, then, similarly, the results would not be accurate. The value of the survey depends on accurate definitions of the factors being correlated. But these definitions are provided by classical method. Therefore, it is classical method that suggests which correlations might be significant. Many possible statistical relations could be investigated. Throw in traits like ‘marital status’ or ‘knowledge of foreign languages’ as a control factor in the survey of incidence of lung cancer, and the distribution should be random: it is hard to conceive of a possible positive correlation between these factors and lung cancer. It is classical method investigating cancer which suggests that certain chemicals in cigarette smoke could be causative factors and are worth investigating.

4. Complementarity in modes of abstraction. Both classical and statistical methods lead to abstract laws. Classical method leads to abstract correlations between variables; statistical method leads to ideal probabilities. Both methods abstract from the concrete in the enriching leap of insight. Both need further insights to be applied in [146] any particular concrete situation. Classical laws are applied to the systematic; statistical laws determine ideal frequencies from which actual frequencies diverge nonsystematically. Both are legitimate scientific procedures; both yield abstract ideal laws or frequencies. But they are applied to the concrete in different ways. Classical laws are applied on the proviso of other things being equal. Statistical laws are applied on the assumption that actual frequencies will diverge nonsystematically from the ideal. The complete view demands the use of both methods in complementarity.

5. Complementary in verification. Both classical and statistical laws can be verified and so both are valid scientific methods. Classical laws determine what would happen if conditions were fulfilled. Statistical laws determine how often one may expect the conditions to be fulfilled.

Classical laws cannot explain everything; they are verified only with the proviso that 'other things being equal'. But it is impossible to exclude all extraneous influences. They have to be reduced to a minimum; and even then allowance has to be made. Classical laws are verified in that the concrete converges on the abstract; but there can be no total coincidence between the concrete and the abstract.

Statistical laws determine how often the conditions will be fulfilled; they indicate which data are due to randomness and which seems to have a significance of its own. What in one age is dismissed as due to inaccuracies of measurement can later be the ground for important discoveries. If a divergence of data from the expected is found to be systematic, then it is an indication that classical law is at work; it is statistics which determines which divergences are random and which are of significance.

6. Complementarity in data explained. There are not two distinct and separate sets of data, one for classical investigation and one for statistical investigation. There is one set of data and certain aspects of the data receive the classical type of explanation while other aspects of the same data are explained along statistical lines.

Data which are systematic at the moment can become nonsystematic, and vice versa. The solar system is systematic for the present, but the planets are slowly coming nearer to the sun and will [147] eventually collapse back and the solar system with all its regularity and system will become a chaos.

What is coincidental can suddenly become systematic. The stray elements of pressure, wind, moisture, and temperature, which are independent and nonrelated, can suddenly become systematically related in the system of a typhoon. Then the different elements do form part of a system of interrelated factors.

In a particular study either classical or statistical method may predominate for a time. But when the aspects of the data dealt with in classical method have been fixed there will be a need for statistical method and vice versa. There are no data which can be entirely explained by one method to the exclusion of the other.

6. The Empirical Residue

Finally, we deal with a notion which some deem to be difficult. The term ‘empirical residue’ refers to what is left over when classical and statistical methods have run their course. Is there something left over? What is it? Can it be explained? There is something left over and we are calling it the empirical residue and by definition it cannot be explained either in terms of classical or statistical method. The empirical residue, then, has three characteristics, (1) it is positive empirical data, (2) it does not possess any immanent intelligibility, but (3) it is connected with a compensating higher intelligibility.⁷

The empirical residue is positive data. It is not simply a vacuum or an absence of data. The positive data are given in experience, by way of the external and internal senses. We do experience a vast multiplicity of data, but experience alone is not understanding; human understanding is insight into data. Experience is preintellectual and preconceptual. An animal can experience but cannot understand. Similarly we experience a vast panorama of data; some of it we understand by classical laws and some of it we understand by statistical laws, but then there remain aspects of the data which can only be experienced. [148]

The data that belongs to the empirical residue possess no immanent intelligibility. They cannot be brought under law. They can be named, pointed to, but they are not the content of a direct insight. The most obvious case of the empirical residue is particular places and particular times. Particular places and particular times are precisely what are abstracted from in the procedures of classical and statistical investigation; they are left out, left behind as not relevant.

Scientific generalization consists in abstracting from individuality. When Galileo discovered his law of falling bodies, he did not have to formulate different laws for different materials, different laws for different places and times. It was an abstract intelligibility which, given certain conditions, could be applied to any material at any time and any place. Particular time, particular place and particular material used were irrelevant, to be excluded, of no significance. This is the power of scientific generalization. The particular as particular, the concrete as concrete, merely numerical differences cannot be brought under law. To bring under law is to abstract the universal from the particular. If you are trying to understand the particular as particular, then, there is no way you can abstract from the particularity.

The Scholastics of the Middle Ages had great difficulty formulating a principle of individuality. They felt that there had to be a reason for the numerical differences of individuals within a species. Aquinas appealed to materia signata quantitate (quantified matter); Scotus appealed to haeccaetas (thisness). But there is no principle that explains merely numerical differences; they are different simply as a matter of fact. The Scholastics were not familiar with the experience of inverse insight into the lack of an expected intelligibility. They were looking for something which was not to be found.

The process of abstraction abstracts the relevant from the irrelevant, the important from the unimportant, the rational from the irrational, the meaningful from the nonsense, the significant from the insignificant. What do we do with the unimportant, the irrational, the nonsense, and the insignificant? Can we bring them under law? Can we explain them? Can we formulate a theory about [149] nonsense? But to formulate a theory means to abstract the intelligible from the unintelligible; there comes a point when the unintelligible is simply unintelligible; when nonsense can be pointed to but cannot be explained. These are precisely the elements that are left behind in the process of human understanding.

The empirical residue is connected with a compensating higher intelligibility. Although the empirical residue possesses no immanent intelligibility of its own, it provides the materials for the procedures of abstraction and generalization that are of enormous significance.

The empirical residue is not a direct correlative of inverse insight. We defined inverse insight as the absence of an expected intelligibility. The difficulty with the empirical residue is that nobody expects it to be intelligible. Few people expect scientific theories to be different depending on where they were invented or by whom or when. Few people look for an ultimate explanation of why x was born at a particular place at a particular time. But one characteristic of the empirical residue is that it is connected with a compensating higher intelligibility. The empirical residue is 'significant' because it allows the process of scientific generalization and abstraction to take place and is itself simply left aside.

The empirical residue is roughly equivalent to what Aristotle referred to as matter. However, he used that term in many different senses such as prime matter and secondary matter, general matter and specific matter; he used it in a technical sense and in a loose sense. The common element was that matter was not knowable; it was the matter in which the form was realized; we know the form in the matter but the matter in itself is strictly, in principle, unknowable. So for us the empirical residue is a technical term with a very specific meaning as what is left over when all intelligibility has been abstracted; data which can be experienced but cannot be explained. The empirical residue can only be known in that strange indirect way of an inverse insight; it can only be known when the processes of generalization and abstraction leave behind the individuality of particular times and places. [150]

In conclusion, lest we get distracted from our main purpose, let us remember that we are slowly becoming aware of how our minds work, how we understand. This chapter compels us to face the fact that there are types of intelligibility, degrees to which data can be brought under law. The scientists of the nineteenth century looked forward to a time when science would understand and therefore control and predict everything. In the twentieth century science is more realistic. We have accepted the need for statistical method as a necessary complement to classical method. There are degrees of intelligibility. Some questions we can answer and some not, not just from lack of information but in principle. Inverse insights bring us up short. They remind us of the limits of our human understanding and the degrees of intelligibility of our universe. We are forced to use at least two different methods in the understanding of any set of data. The exercises give you some opportunity to identify these procedures in your own consciousness.

The principle of sufficient reason seems to imply that there is a reason for everything. The foregoing has revealed that there are degrees of intelligibility ranging from the satisfying intelligibilities of classical laws to the shock of the empirical residue which lacks immanent intelligibility. We have a reluctance to accept the unintelligibility of some data; we spontaneously ask for the reason for something and expect a direct insight. But this is not always possible. For there is chance; there are random occurrences; there is no explanation for the particular as particular, for particular times and places; there are accidents, coincidences, the merely empirical residue. Why did the locusts land on my farm and not on my neighbors? Why did I get malaria and my friend in the same room did not? Why did the tree fall just when I was passing? Why did it rain here and there is drought over there? This is the kind of world we live in; one which is a complicated combination of the systematic and nonsystematic; one which can only be correctly understood by a combination of direct, devaluated, or pure inverse insights.

We got into some rather technical 'stuff' in this chapter. The more down-to-earth examples of the bewildered reader, the unfortunate secretary and the inveterate gambler at the beginning [151] remind us that the problem exists not only for scientists and experts but for everyone of common sense. The type and degree of understanding attainable in any discipline will vary enormously. Aristotle warns the readers of his Ethics not to expect the same degree of precision in moral inquiry as is possible in mathematics. We noted that measurement and hence precision and accuracy is primary in the physical sciences; but in the human sciences it is explanatory definition which is primary. In all areas we must differentiate what we can know with certainty, with high probability, or simply as probable. In all cases we distinguish what we can understand clearly and distinctly and what we can only expect to understand in a confused and ambiguous manner. Our universe is rich in the diversity of the phenomena it presented to us. Our minds are rich in strategies for coping with this diversity. The third stage of meaning is not some monochrome reduction to one type of meaning; rather a nuanced and sophisticated grasp of the complications, variety and levels of intelligibility of human persons operating in the real world.

Comments on Exercises

7. Weather forecasts give a range of probabilities rather than certainties. Rain may be possible, probable or highly probable or anything in-between. The probable path of a tornado can be predicted but with little precision or certainty. It is an illusion to think you can have all the relevant information: for that you would need to know everything about everything. [153]

End Notes.

^1. Insight, 44.

^2. Insight, 43-50.

^3. Insight, 126.

^4. Pierre-Simon Laplace (1749-1827), French mathematician, astronomer and physicist. He applied Newton's laws of motion to the planets with a great degree of accuracy.

^5. Insight, 126. The word 'statistics' sometimes refers to information in a statistical form or to the mathematics involved in dealing with probabilities. We use it in the sense of the prior understanding or mentality behind dealing with probabilities.

^6. Gregor Mendel (1822-1884), an Austrian botanist, studied the occurrence of traits of tallness and color in generations of peas. He noticed the statistical significance of the patterns of recurrence and formulated laws of genetics, leading to the discovery of genes.

^7. Insight, 50-56.

Go Back