that affirming the consequent is not a valid deductive form.
If X, then Y.Y.Invalid: Therefore, X.
That the prediction has been confirmed merely offers tentative confir-mation of the hypothesis. The hypothesis survives for the moment, butmore stringent tests of it are sure to follow.
Whewell agreed with Hume that inductive conjecture was a habit ofhuman thought, but the habit that Hume so disparaged Whewell regardedas fruitful though mysterious. He was unable to explain the mentalprocesses that gave rise to such creative thoughts though he believed in-ductive generalization was part of it. He called the ability to conjure a hy-pothesis an unteachable inventive talent but a crucial one, because,without a hypothesis, a set of observations remain nothing more than adisconnected collection of facts that could neither predict nor explain.However, with one came the breakthroughs of science.130 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
Whewell’s description of this crucial creative aspect of science remindsme of a conversation I had many years ago with Charles Strauss, a prolificcomposer of some of Broadway’s most successful musicals. In my earlytwenties at the time, short on tact and long on audacity, I asked him to ex-plain how he managed to be so productive. With more forbearance than Ideserved, Charles described a daily discipline of religiously sitting at the pi-ano from 8 until 11 each morning and from 2 until 5 each afternoon,whether he felt inspired or not. He told me he treated composing music likea job. During this time he would test tentative melodies—musical hypothe-ses. He modestly attributed his high productivity to 99 percent disciplineand 1 percent creative talent. However, as I thought about this conversationyears later, it seemed otherwise. With an almost infinite number of notecombinations, his success at creating infectious tunes had to be a creativetalent—some special ability to see which of those musical conjectures hadthe potential to be melodies and which did not. This was what Whewellcalled the happy guess—that unteachable talent of seeing a theme thatmeaningfully relates a disparate set of observations or notes that eludes theaverage person. A few folks have it. The vast majority do not.
Whewell’s realization that proposing a hypothesis was an act of inven-tion no less than the creation of the steam engine or the light bulb repre-sented a profoundly important advance in thinking about science.”Induction could not produce truths on its own, but it was a necessary firststep. This was a profound departure from the prior notion of science as asystematic objective investigation followed by inductive generalization.Whewell saw the scientist as a creator as much as an investigator.
Karl Popper: Falsification and Bringing DeductionBack into Science
In two landmark works, The Logic of Scientific Discovery” and Conjec-tures and Refutations,”> Karl Popper (1902-1994) extended the insight ofWhewell and redefined the logic of scientific discovery by clarifying therole of deduction. Popper's central contention was that a scientific inquirywas unable to prove hypotheses to be true. Rather, science was limited toidentifying which hypotheses were false. This was accomplished by usingobserved evidence in combination with the valid deductive form of falsifi-cation of the consequent.
If hypothesis H is true, then evidence E is predicted to occur underspecified conditions (e.g., back test of a TA rule).
Evidence E did not occur under the specified conditions.Therefore, hypothesis H is false.The Scientific Method and Technical Analysis 131
In taking this stance, Popper challenged the prevailing view advo-cated by a school of philosophy called logical positivism. Just as FrancisBacon had revolted against the strictures of the Greek tradition, Popperwas in revolt against the Vienna Circle, the home of logical positivism.Logical positivists believed that observations could be used to prove hy-potheses to be true. Popper demurred, saying that observed evidencecould only be used to prove a hypothesis false. Science was a baloney de-tector, not a truth detector.
Popper justified his method, called falsificationism, as follows. Agiven set of observations can be explained by or is consistent with numer-ous hypotheses. Therefore, the observed data, by itself, cannot help us de-cide which of these hypotheses is most likely correct.?’ Suppose the datais that one of my shoes is missing. One hypothesis that might explain thisobservation is that I’m a disorganized housekeeper who is always misplac-ing things. Another hypothesis, which is equally consistent with the obser-vation of a missing shoe, would be that my house was burglarized by aone-legged thief who only had use for one shoe. In fact, an infinite numberof hypotheses could be proposed that are consistent with (explain) themissing shoe.
We have already seen that data cannot be used logically to deduce thetruth of a hypothesis. Attempts to do so commit the fallacy of affirmingthe consequent.”? However, and this is the key to Popper's method of falsi-ficationism, data can be used to validly deduce the falsehood of a hypoth-esis by denial of the consequent. In other words, disconfirming evidencecan be used to reveal a false explanation. For example, finding the othershoe would be evidence that would falsify the one-legged thief hypothesis.The logical argument is as follows:
Premise 1: If a one-legged thief is responsible for my missing shoe,then I will not find the shoe in my home.
Premise 2: Missing shoe is found (consequent denied).Conclusion: Therefore, the one-legged thief hypothesis is false.
Popper's method of falsification runs against the grain of commonsense, which is biased in favor of confirmatory evidence. As pointed out inChapter 2, intuition often tells us to test the truth of a hypothesis by see-ing if confirmatory evidence can be found. We do so under the mistakenimpression that confirmatory evidence is sufficient to establish truth.However, using evidence in this way commits the fallacy of affirming theconsequent. We are not wrong to suspect that confirmatory evidenceshould be found if the hypothesis is true, but we are wrong to think thatconfirmatory evidence is sufficient to establish its truth. Said differently,132 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
confirmatory evidence is a necessary condition of a proposition’s truth butnot a sufficient condition.”’ Popper’s point was that the absence of neces-sary evidence is sufficient to establish the falsity of a hypothesis, but thepresence of necessary evidence is not enough to establish its truth. Hav-ing four legs is a necessary condition of a creature being a dog, but thepresence of four legs is not sufficient to establish that a creature is a dog.However, the observation that the creature does not have four legs is suffi-cient to falsify the contention it is a dog. Popper's logic of falsification canbe seen as a protection against the confirmation bias that infects informalinference (see Chapter 2).
One final example may help clarify the power of falsifying evidenceand the weakness of confirmatory evidence. It is the famous problem ofthe black swan posed by philosopher John Stuart Mill (1806-1873). Sup-pose we wish to ascertain the truth of the proposition: ‘All swans arewhite.’ Mill said, and Popper concurred, that no matter how many whiteswans have been observed—that is, no matter how voluminous the confir-matory evidence—the proposition’s truth is never proven. A black swanmay lurk just around the next corner. This is the limitation of inductionthat so upset Hume. However, by merely observing a single non-whiteswan, one may declare with certitude that the proposition is false. Theconditional syllogism below shows that the falsity of the proposition Allswans are white is based on the valid deductive form falsification of theconsequent.
Premise 1: [fit is true that all swans are white, then all future obser-vations of swans will be white.
Premise 2: A nonwhite swan is observed.Valid Conclusion: The proposition that all swans are white is false.
The general form of argument used to test a hypothesis under Pop-per’s method of falsification is:
Premise 1: [f the hypothesis is true, then future observations are pre-dicted to have property X.
Premise 2: An observation occurs that lacks property X.Valid Conclusion: Therefore, hypothesis is false.
As will be seen in Chapters 4 and 5, this is the logic used to test statis-tical hypotheses.
The Provisional and Cumulative Nature of Scientific Knowledge.One implication of Popper’s method of falsification is that all existing sci-
 The Scientific Method and Technical Analysis 133
entific knowledge is provisional. Whatever theory is currently accepted ascorrect is always a target for empirical challenge and the possibility of itsbeing replaced by a more correct theory always exits. Today Einstein’sTheory of Relativity is taken as correct. Though its predictions have with-stood countless tests, tomorrow a new test may show it to be false or in-complete. Thus, science is a never-ending cycle of conjecture, prediction,testing, falsification, and new conjecture. It is in this way that the body ofscientific knowledge continually evolves toward an ever more accuraterepresentation of objective reality.
In most instances when older theories are replaced, it is not becausethey are proven false so much as they are shown to be incomplete. WhenNewton's laws of motion were replaced by Einstein’s theories, Newtonianphysics was still mostly correct. Within the limited domain of everyday ex-perience, where bodies travel at normal speeds (approximately less than90 percent of the speed of light), Newton’s laws still held true. However,Einstein’s theory was correct in a wider domain, which not only includedthe motions of everyday objects, but also objects traveling up to and in-cluding the speed of light. In other words, Einstein's theory, which builtupon Newton's, was more general and thus subsumed it.
The net result of building upon prior successful ideas (those whosepredictions have been confirmed) and pruning away wrong ideas (thosewhose predictions have been falsified) is a body of knowledge that con-tinually improves. This cannot be said of any other intellectual discipline,where new styles are introduced but where newer is not necessarilybetter. The fact that a scientist in any field of science knows more todaythan even the best one living just a generation ago is beyond debate.However, whether gangsta rap is better or worse than Mozart could beargued endlessly.
The Restriction of Science to Testable Statements. Anotherimplication of Popper’s method is that science must restrict itself totestable hypotheses—propositions that generate predictions about obser-vations not yet made. To say that a hypothesis has been tested and hassurvived or has been tested and falsified means that predictions deducedfrom it have either been confirmed or contradicted by new observations.The comparison of predictions with new observations is the crucial mech-anism that fosters the continual improvement of scientific knowledge. Forthis reason, propositions that do not generate testable predictions mustbe excluded from the domain of scientific discourse.
The term prediction, in the context of hypothesis testing, warrantssome clarification, because TA is essentially an enterprise dedicated toprediction. When we speak of prediction as it pertains to testing a hypoth-esis, it does not necessarily mean foretelling the future though the pre-134 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
dicted observations may indeed lie in the future. Rather, the term predic-tion refers to the fact that the observations’ outcomes are not yet known.
Predictions made in historical sciences, such as geology, refer toevents that have already taken place perhaps eons ago. For example, geol-ogy’s dominant theory, plate tectonics, may predict that specific geologicformations that were created millions of years ago would be observed ifan investigation of some specific location were to be carried out tomor-row. In finance, the efficient market’s hypothesis predicts that if a TA rulewere to be back tested, its profits, after adjustment for risk, would not ex-ceed the risk adjusted return of the market index.
Once the prediction has been deduced from the hypothesis, the opera-tions necessary to produce the new observations are carried out. Theymay involve a visit to the location of the predicted geologic formation orthe back test of the TA rule. Then it becomes a matter of comparing pre-diction with observation. Measuring the degree of agreement between ob-servation and prediction and making the decision about whether thehypothesis is left intact or falsified is what statistical analysis is all about.
What is important from the standpoint of science is that the hypothe-sis is able to make predictions about observations whose outcomes arenot yet known. This is what allows a hypothesis to be tested. Observationswhose outcomes are known cannot serve this purpose because it is al-ways possible to fashion an explanation, after-the-fact that is consistentwith said observations. Therefore, activities that are unable or unwillingto make testable predictions, thus opening them to the possibility of falsi-fication, do not qualify as scientific.
The Demarcation Problem: Distinguishing Science from Pscu-doscience. An important consequence of Popper’s method was that itsolved a key problem in the philosophy of science—defining the boundarybetween science and nonscience. The domain of science is confined topropositions (conjectures, hypotheses, claims, theories, and so forth) thatmake predictions that are open to refutation with empirical evidence.Popper referred to such propositions as falsifiable and meaningful. Propo-sitions that cannot be challenged in this manner are unfalsifiable or mean-ingless. In other words they do not say anything of substance, somethingthat sets up a testable expectation.
Unfalsifiable propositions may appear to assert something, but, infact, they do not. They cannot be challenged because they do not say, withany degree of specificity, what can be expected to happen. In effect, theyare not informative. Hence, the falsifiability of a proposition is related toits information content. Falsifiable propositions are informative becausethey make specific predictions. They may prove to be wrong, but at leastthey say something of substance. A proposition that cannot generate falsi-The Scientific Method and Technical Analysis 135
fiable predictions essentially says any outcome can happen. For example,a weather forecast that says, It will be cloudy or sunny, wet or dry,windy or calm, and cold or hot, allows for all possible outcomes. It can-not be falsified. The only thing good that can be said about it is that itslack of information is completely obvious.
The problem is that pseudoscientists and pseudoforecasters areclever about the way they word their predictions so as to obscure theirlack of information and nonfalsifiability. Consider the astrologer’s forecast“You will meet a tall dark stranger, and your life will be changed.” Thisstatement is impossible to refute no matter what the outcome. Should youever go back for a refund, you will be given one of two answers: (1) be pa-tient—you will meet the stranger soon, or (2) you already did meet a darkstranger and you life is indeed different, but you are oblivious to thechange. These answers cannot be challenged because the prediction wasvague. It neither stated when your life would change or in what measur-able (verifiable) manner. This is a far cry from the prediction, “Before 7:00PM. next Wednesday you will see a man wearing one red shoe, walkingeast on 42nd Street whistling ‘Satin Doll.’” By 7 p.M. next Wednesday, theevidence will be in and you will have had the opportunity to evaluate theprediction as true or false. Even if the prediction turns out to be false, itwas at least falsifiable allowing you to decide if future visits to the as-trologer are worthwhile.
The infomercial pitchmen that inhabit late-night television are mas-ters of the meaningless claim. “Wearing our copper bracelet will im-prove your golf game.” What does improve mean? How is it to bemeasured and when? As with the astrologer’s forecast, the claim’svagueness makes it impossible to deduce a testable (falsifiable) predic-tion. However, it is quite easy to imagine after-the-fact anecdotal evi-dence that is seemingly confirmatory. “Holy cow, I feel so much morerelaxed wearing my copper bracelet. I used get all tensed up before tee-ing off. Now my drives seem straighter. Last week I almost had a hole inone, and I don’t curse nearly as much as I used to. My wife even says Ilook more handsome in my golf outfit, and I think my hair has stoppedfalling out.” Although meaningless claims invite confirmatory anecdotes,they are protected from objective falsifying evidence. The claimantnever gets nailed and the claim gains support from seemingly confirma-tional anecdotal reports.
In contrast, the statement, “Wearing a copper bracelet will increasethe length of your drives by 25 yards,” is informative and meaningful be-cause it generates a testable prediction. You hit 100 golf balls not wearingthe copper bracelet and determine their average distance. The next day,do the same thing while wearing a copper bracelet and get their averagedistance. Repeat this alternating series of experiments for 10 days. If the13@ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
drives on copper-bracelet days are less than 25 yards better, evidence re-futing the claim would be in hand.
Limitations of Popper’s Method. As important as Popper’s methodof falsification is to modern science, it has been criticized on a number ofgrounds. Critics assert that Popper’s contention that hypotheses can bedefinitively falsified overstates matters. Although the observation of ablack swan can neatly and logically falsify the universal generalizationthat all swans are white, the hypotheses of real science are far more com-plex” and probabilistic (nonuniversal). They are complex in the sensethat a newly proposed hypothesis rests on numerous auxiliary hypothesesthat are assumed to be true. Thus, if a prediction deduced from the newhypothesis is later falsified, it is not clear whether the new hypothesis wasin error or one of the many auxiliary hypotheses was incorrect. This provedto be the case when the solar system’s eighth planet, Neptune, was discov-ered. The aberrant path of Uranus was not due to flaws in Newton's lawsbut in the auxiliary hypothesis that the solar system contained only sevenplanets. However, when the theory did fail early in the twentieth century,it was indeed because of imperfections in Newton's laws. Doing real sci-ence is tricky business. TA is a long way from facing these problems be-cause we are still at the at the point of scratching the data for reliablepredictive rules.
Moreover, because many of the hypotheses of science are probabilis-tic, as would be the case in TA, an observation that contradicts the hy-pothesis can never be taken as certain evidence of falsity. The aberrantobservation may be a chance occurrence. It is here that statistical analysisenters the picture. As will be pointed out in Chapters 4 and 5, the decisionto reject a hypothesis on the basis of observed evidence runs a certainprobability of being wrong. Statistics helps us quantify this probability.
Despite these and other limitations that go beyond our concerns here,Popper’s contributions to the development of the scientific method havebeen enormous.
The Information Content of Scientific Hypotheses
To recap, a hypothesis is informative if it can make testable predictions.This opens it to the possibility of being found false. Thus, the falsifiabilityof a hypothesis and its information content are related.
Within the domain of scientifically meaningful hypotheses, there aredegrees of information content and falsifiability. Some hypotheses aremore information rich and hence more falsifiable than others.
When Popper referred to a bold conjecture he was speaking of ahighly informative hypothesis from which many falsifiable predictionsThe Scientific Method and Technical Analysis 137
could be deduced. The scientist’s job, therefore, is to continually attemptto refute an existing hypothesis and to replace it with an even more infor-mative one. This spurs the improvement of scientific knowledge.
An information-rich hypothesis makes many precise (narrow ranged)predictions about a broad range of phenomena. Each prediction presentsan opportunity to show the hypothesis is false. In other words, the moreinformative a hypothesis the more opportunities it presents for falsifica-tion. In contrast, low information, timid hypotheses make fewer or less-precise predictions. Consequently they are more difficult to falsify. Forexample, a TA rule that claims high profitability on any instrument in anytime frame makes a bold and information-rich claim that that could be fal-sified by showing that it is unprofitable on one market in one time frame.In contrast, a method that only claims to be marginally profitable on S&Pfutures on the one-week bar time scale is timid, has low information con-tent, and is hard to falsify. The only opportunity to refute it would be lim-ited to back test of the S&P 500 on a weekly showing that was not at allprofitable.
Some TA methods that are seemingly informative are not. Elliott WavePrinciple is a case in point. On the surface, it bravely proclaims that allprice movement in all markets over all time scales can be described by asingle unifying principle. This proposition is seemingly confirmed by theability of EWP practitioners to produce a wave count for any prior seg-ment of price data. In fact, EWP is timid to the point of being meaninglessbecause it makes no falsifiable predictions of future price motion.!
A second case in point, one more pleasing to the TA community, is theEfficient Markets Hypothesis (EMH). In this context, the term efficiencyrefers to the speed with which prices reflect all known and knowable in-formation that is relevant to the future returns of an asset. In an efficientmarket, relevant information is presumed to be reflected in price almostinstantaneously. EMH comes in three flavors. In descending order of bold-ness, information content, and falsifiability they are: EMH strong, EMHsemistrong, and EMH weak.
EMH strong asserts that financial markets are efficient with respect toall information, even private inside information. This version predicts thatall investment strategies, be they based on an inside tip from the presidentabout an impending takeover, or based on public information of a funda-mental or technical nature, will be useless in earning market-beating (ex-cess) returns. This most audacious version of EMH is also seemingly themost informative and falsifiable because any evidence of abnormal profitsfrom any investment strategy whatsoever, irrespective of the type of infor-mation or form of analysis used, would be sufficient to refute EMH strong.However, because information that is known privately can never be con-firmed, this version is not testable in a practical sense.138 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
The semistrong version of EMH makes a less informative and nar-rower claim, saying that the market is only efficient with respect to publicinformation. This version of EMH can be falsified with any evidence ofmarket-beating returns produced by an investing strategy based on eitherpublic fundamental data (P/E ratios, book values, and so forth) or techni-cal data (relative strength rank, volume turnover ratios, and so forth). Ineffect, EMH semistrong denies the utility of fundamental and technicalanalysis.
Finally we have EMH weak, which makes the least bold and least in-formative claim. It asserts that the market is only efficient with respect topast price, volume, and other technical data. Because EMH weak only de-nies the utility of technical analysis, it presents the smallest and hardest-to-hit target by would-be falsifiers. Their only hope would be to presentevidence that shows excess returns generated by an investment strategybased on TA.
Because EMH weak is the hardest version to falsify and is thus theleast likely to be proven false, its falsification would also generate themost surprise. In other words, of all versions of EMH, falsification of EMHweak would generate the biggest increase in knowledge. This points out ageneral principle of science: the largest gains in knowledge occur whenthe most timid and hardest to falsify hypotheses are falsified. A test show-ing that inside information, like the tip from a corporate president, wasable to generate excess returns (i.e., falsification of EMH strong) wouldnot be surprising nor would we learn very much from it. Big deal, so in-side information generates profits. What else is new? In contrast, the falsi-fication of EMH weak would be a highly informative event for both TApractitioners and EMH supporters. Not only would it mean the final de-struction of the EMH, an important principle of finance for over 40 years,but it would be an important confirmation of TA’s validity. Both would rep-resent large changes in the current state of knowledge.
Thus, it can be said that the gain in knowledge that occurs upon falsi-fication of a hypothesis is inversely related to its information content.Likewise, it can be said that the knowledge gained by confirmation of ahypothesis (observations consistent with its predictions) is directly re-lated to the information content of the hypothesis. The most informativehypotheses make the most audacious claims of new knowledge. They at-tempt to bring within the realm of understanding the broadest range ofphenomena with the greatest degree of precision while at the same timeinvolving the fewest assumptions. When such a hypothesis is falsified, weare not very surprised nor do we learn very much. Few would have ex-pected the hypothesis to be confirmed, except perhaps for the bold scien-tist proposing it. For example, suppose a bold new theory of physics is putforward, one of whose predictions is that it is possible to build an anti-The Scientific Method and Technical Analysis 139
gravity device. If true, such a theory would represent a major increase inknowledge. However, if the device fails to work, no one would be sur-prised by the prediction’s failure. However, it is exactly the opposite whena timid hypothesis is falsified. For example a timid hypothesis would beone that merely asserts that the currently accepted theories of physics aretrue and predicts that the antigravity device should fail. The falsificationof this weak hypothesis via observations of the antigravity device workingwould result in a very significant gain in knowledge—the verification ofnew physics.
The most timid hypothesis that can be put forward is one that assertsthat there have been no new discoveries. In other words, it says that allthat is currently known is all that there is to know. This hypothesis deniesthe truth of any other hypothesis that asserts that something new hasbeen discovered. The timid hypothesis that asserts that nothing new hadbeen discovered has a special name in science. It is called the null hypoth-esis and it is the starting assumption in the investigation of any claim thata new discovery has been made. Whether that claim asserts that a newvaccine will cure a dreaded disease, that a new principle of physics tellsus how to nullify gravity, or that a TA rule has predictive power, we alwaysstart by assuming that the null hypothesis is true. Then, if evidence can beproduced that falsifies the null hypothesis, a most timid claim, it generatesa big gain in knowledge.
Thus, science proceeds as follows. Every time a bold new hypothesisis put forward, it spawns an opposing claim, the null hypothesis. The nullis as timid as the new hypothesis is bold. Jonas Salk’s bold hypothesis thathis vaccine would prevent polio better than a placebo spawned a compet-ing claim, the null hypothesis. It made the timid prediction that the vac-cine’s ability to prevent infection would be no better than a placebo. Thiswas a timid prediction because every prior attempt to develop a vaccineagainst polio had failed. These two competing claims left no middleground. If one hypothesis could be falsified, by logic’s Law of the Ex-cluded Middle, we know that the other must be true. Salk’s experimentalevidence made it clear that the rate of infection among those receiving thereal vaccine was significantly less than those receiving the placebo. Inother words it was sufficient to falsify the null’s prediction. This was a sur-prising result that represented a huge increase in medical knowledge!
How Scientists Should Respond to Falsification
How should a scientist respond when a hypothesis or theory that has sur-vived many prior tests is ultimately falsified because recent observationsconflict with predictions? The proper response is whatever leads to the140 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
biggest increase in knowledge. Because scientists are human beings, theysometimes fail to do what is scientifically correct.
There are two possible responses that increase knowledge. The firstis to preserve the existing hypothesis by using it to predict new, previ-ously unknown facts. If these new facts are confirmed and can explainwhy the observations that had been in conflict with the hypothesis are nolonger in conflict, then the hypothesis deserves to be retained. The newfacts represent an increase in what we know about the world. A secondproper response is to throw out the old hypothesis and propose a newone that not only accounts for all observations that had been explainedby the prior hypothesis but also explains the new discordant observa-tions. This also represents an increase in knowledge in the form of anewhypothesis with greater explanatory or predictive power. However, in ei-ther case the correct response is to do whatever advances the frontier ofknowledge the most.
Unfortunately the interests of science sometimes take a back seat topersonal agendas. Human nature gets in the way of good science. Emo-tional, economic and professional ties to a prior hypothesis can motivateattempts to explain away the discordant evidence in a way that reducesthe information content and falsifiability of their cherished hypothesis.This moves the frontier of knowledge backward. Fortunately science is acollective self-correcting enterprise. The community of scientists happilytakes their fallen brothers to task when they stray from the path of right-eousness in this way.
Some examples will clarify these abstract concepts. First, I present anexample of a proper response to predictions being contradicted by newobservations. In this case, new facts were predicted to rescue an estab-lished theory from falsification. This high road was taken by two as-tronomers during the nineteenth century, and it led to new knowledge inthe form of a new planet being discovered. At that time, Newton's laws ofmotion and gravity were the accepted physics of planetary motion. Theyhad been confirmed and reconfirmed by countless observations, but then,to the surprise of astronomers of that day, new and more powerful tele-scopes showed the planet Uranus was deviating from the orbit predictedby Newton’s laws. A rigid and improper application of falsificationismwould have called for an immediate rejection of Newtonian mechanics.However, all theories rest on a bed of auxiliary assumptions. A key as-sumption in this case was that the solar system contained only seven plan-ets with Uranus being the seventh and furthest from the sun. This ledastronomers Adams and Leverrier to boldly predict the existence of a yetundiscovered eighth planet (the new fact), lying beyond Uranus. If thiswere true, that new planet’s gravitational effects could explain the aber-rant motion of Uranus, which seemed to be in conflict with Newton’s laws.The Scientific Method and Technical Analysis 141
In addition, if an eighth planet did exist, one of Newton’s laws would pre-dict the exact spot in the sky where the new planet would be observable.This was a bold, informative, and highly falsifiable prediction that putNewton’s laws to a most stringent test. The astounding prediction madeby Adams and Leverrier about where the new planet would appear in thesky was indeed confirmed in 1846 with the discovery of Neptune. Theysaved Newton’s laws from falsification by demonstrating that the theorywas not only able to explain the deviant behavior of Uranus but was alsoable to make a highly accurate prediction. This is the kosher way to retaina theory when it is confronted with dissonant observations.
In the end, however, Newton’s laws proved to be provisionally true, asis ultimately the case for all laws and theories. Although Newton's lawshad worked perfectly for more than 200 years, early in the twentieth cen-tury more precise astronomical observations were found to be truly in-consistent with the theory's predictions. The old theory had