A Critique of the Hypothesis, and a Defense of the
Question, as a Framework for Experimentation
David J. Glass
Scientists are often steered by common convention,
funding agencies, and journal guidelines into a
hypothesis-driven experimental framework, despite
Isaac Newton’s dictum that hypotheses have no place
in experimental science. Some may think that New-
ton’s cautionary note, which was in keeping with an
experimental approach espoused by Francis Bacon, is
inapplicable to current experimental method since, in
accord with the philosopher Karl Popper, modern-day
hypotheses are framed to serve as instruments of falsi-
ical rationalist” framework too is problematic. It has
been accused of being: inconsistent on philosophical
experimental science, which is verification and not
falsification; and harmful to the process of discovery
as a practical matter. A criticism of the hypothesis as a
framework for experimentation is offered. Pre-
sented is an alternative framework—the query/
model approach—which many scientists may dis-
cover is the framework they are actually using, despite
being required to give lip service to the hypothesis.
© 2010 American Association for Clinical Chemistry
In the early 1600s, Francis Bacon, the Lord Chancellor
to King James I of England, was arrested for accepting
bribes, briefly imprisoned, and forbidden thenceforth
to hold office. He thus found himself with ample time
ganon, which he criticized for its nonempirical ap-
proach to scientific exploration (1).
method can be boiled down to 2 main facets: (a) a call
for an inductive, rather than a deductive, approach to
science and (b) an advocacy for a reliance on experi-
ments rather than dogma for induction.
tion means that what happened can be said to be pre-
dictive of what will happen, so that one can induce
from the experience that a released apple falls toward
the ground that the next time one releases the apple it
will fall again. Second, induction refers to the ability to
ered by experiments on a specific case, so the fact that
an apple fell might allow the scientist to induce that an
one did the original experiments on an apple, not an
As for then-current practice, Bacon further
pointed out the problem with starting with an un-
proven premise and deducing rules from that premise.
If the premise is unfounded, then the resulting deduc-
tions from that premise will be equally unfounded. Al-
it was the thing he was criticizing, hypothesis being
defined as “whatever is not derived from phenomena,
an unproven premise, advanced without evidence, as a
tentative explanation” (3). The hypothesis as a frame-
work was in turn explicitly rejected by Isaac Newton, a
ton’s Principia had been organized around hypotheses
(4). It has been argued that Newton’s evolution from
an alchemist to a scientist forced him to move away
from the hypothesis construct in later editions of his
great work and then in his Opticks to eschew the hy-
pothesis in favor of rules he could prove, rules derived
inductively from experiments. Newton wrote in the
Principia (4) (translated from Latin):
I have not as yet been able to discover the reason for these
properties of gravity from phenomena, and I do not frame
hypotheses. For whatever is not deduced from the phenom-
ena must be called a hypothesis; and hypotheses, whether
chanical, have no place in experimental philosophy. In this
philosophy particular propositions are inferred from the
phenomena, and afterwards rendered general by induction.
Furthermore, when Newton was writing the
Opticks, he started out Part I as follows: “My design in
this book is not to explain the properties of light by
Novartis Institutes for Biomedical Research, Cambridge, MA.
* Address correspondence to the author at: Novartis Institutes for Biomedical
Research, 100 Technology Square, Cambridge, MA 02139. E-mail david.
Received April 5, 2010; accepted April 7, 2010.
Previously published online at DOI: 10.1373/clinchem.2010.144477
Clinical Chemistry 56:7
hypotheses, but to propose and prove them by reason
and experiments” (5).
unproven explanation before one did actual experi-
ments (6) (translated from Latin):
Once a man’s understanding has settled on something, . . . it
it encounters a large number of more powerful countervail-
ing examples, it either fails to notice them, or disregards
them, or makes fine distinctions to dismiss and reject them,
and all this with much dangerous prejudice, to preserve the
authorityofitsfirstconceptions. . ..Andevenapartfromthe
pleasure and vanity we mentioned, it is an innate and con-
stant mistake in the human understanding to be much more
moved and excited by affirmatives than by negatives, when
rightly and properly it should make itself equally open to
both. . . .
It is worth taking some time to unpack Bacon’s
aphorism into a series of arguments and apply them to
Such an action may not happen because of malfea-
sance, but rather because the existence of a hypothesis
drives a particular type of experimental methodology,
including a filter for data interpretation. It creates the
expectation of a particular result and thus implicitly
rejects other possibilities before the actual experiment
For an illustration of the issue, consider the hy-
pothesis that caffeine increases blood pressure (7). It
should be clear that this hypothesis creates the expec-
tation of a particular result, an increase in blood pres-
it should not be controversial to point out, first, that a
scientist starting out with such a framework may be
premise is found than if the framework was simply the
question, “What is the effect of caffeine on blood pres-
in favor of discovering an increase in blood pressure
look for an increase, as opposed to a decrease. In other
words, this hypothesis establishes a data filter that asks
the scientist to determine whether an increase in par-
ticular happened and therefore forces methodology to
determine an increase in particular.
What might a scientist do to “confirm” the hy-
pothesis that caffeine increases blood pressure? One
might keep increasing the caffeine dosage until a “pos-
itive” result is seen, rejecting the finding that lower
doses of caffeine do not significantly increase blood
pressure as “negative” and therefore not germane.
ticular hypothesis might accept even very small in-
creases in blood pressure as positive evidence, even if
such increases are not physiologically relevant. Third,
any discounting evidence, such as the lack of a dose
response, might be ignored in light of single “positive”
tigator has put in place a preexisting requirement to
arrive at a particular result, which a hypothesis as a
framework seems to establish more than does a
The first claim, that the true goal of the scientist is af-
the entire set of clinical trials. Can anyone doubt that
hypothesis verification and not falsification is the goal
of these trials? Why would anyone spend hundreds of
millions of dollars on such an experiment if the goal
were to disprove the framing hypothesis? In addition,
what would be the point of doing a clinical trial if it
were unacceptable to induce from the results, if it were
comes? This issue does not apply just to medical trials.
Consider any scientist publishing in one of the top
journals. What is the percentage of reports that claim
premise falsification, as opposed to a new finding that
demonstrates a rule or principle thought to stably ex-
plain how things work?
Given this simple reality, that scientists do experi-
ments usually because they want to derive an experi-
mental model that has inductive, or predictive,
are we often made to frame our experiments within a
How did this situation develop? This question is
complicated and probably requires a book rather than
an essay to answer in detail. But here is the bare-bones
tale. Shortly after Newton died, David Hume, in A
by declaring that the fact that something behaved in a
certain way in the past is no guarantee that it will do so
in the future, rejecting even probability as a rationale
for engaging in inductive reasoning (8, 9). For the
Clinical Chemistry 56:7 (2010)
reader to appreciate how extreme Hume was in this
claim, it is worth quoting several lines in full.
case; and at the utmost can only prove, that that very object,
which produced any other, was at that very instant endowed
with such a power; but can never prove, that the same power
must continue in the same object or collection of sensible
qualities; much less, that a like power is always conjoined
with like sensible qualities. Should it be said, that we have
experience, that the same power continues united with the
form any conclusion beyond those past instances, of which
we have had experience. (9)
years, the most frequent rejoinder being that probabil-
ity was rational and allowed one to make predictive
statements within appropriate constraints (10–12).
Yet, to be consistent with Hume’s proscription against
induction, the philosopher Karl Popper in the 1930s
espoused an experimental method that reinvigorated
the hypothesis but advocated its use as a pure instru-
ment of falsification, because even if one cannot say
something will happen, at least one can say with cer-
approach placed Popper in a philosophical school
known as critical rationalism.
Critical rationalism has obvious attractions, not
the least of which is the pure mathematical ability to
disprove something definitively, as opposed to the rel-
negative example is required to disprove a hypothesis,
whereas an infinite number of confirming trials still
leaves open what might happen the next time the ex-
periment is performed and thus leaves the scientist
fore, although it is easy to see why the hypothesis-
falsification terminology gained currency—because of
its rigor and its utility at framing an issue so that it can
of pure falsification that can be said to be deceptive, if
that is not how the paradigm is actually being used.
What is more, Popper’s approach has been shown
which is that it is not possible to avoid inductive rea-
soning (14, 15). In addition, falsificationalism can be
subjected to the same semantic maneuvers as verifica-
tion and thus does not offer the scientist any relative
Because scientific experimentation as a rational
endeavor must rely on the predictability of prior expe-
rience to proceed and because it can be demonstrated
tive of the future, within certain probabilities, and
within certain logical limits and parameters, one is left
ence. Even if one cannot say with mathematical cer-
tainty that reality is stable, the repetition of results
(such that statistics and probabilities can be deter-
mined) does in fact constitute control experiments for
the time variable. The scientist demonstrates through
why one must always proscribe one’s claim of verifica-
tion within certain probabilities). Lest you still decry
this approach as irrational in absolute terms, stop and
decision to steel yourself against gravity. How do you
justify the expectation that a car will move when you
insert a key into its ignition? How do you justify the
expectation when you get on board an airplane that it
of induction. Induction is the reasoning by which one
then set within a set time each day. It is a particular
predictive model of reality that is verified on a daily
basis. Induction can thus be shown to be more than a
matter of necessity or of pure convenience but actually
its stability, within accepted constraints.
The US NIH requires hypotheses for most of its grant
applications (17–19). Although hypotheses are con-
structs that need not be derived from phenomena, put
forth as a tentative explanation, to justify funding for
experiments that might derive the relevant phenom-
ena, NIH review committees often require that suffi-
cient preliminary evidence be shown to demonstrate
the hypothesis is probably correct (3, 18, 19).
This situation establishes a couple of things. First,
the term “hypothesis” is not being used correctly be-
manded. Second, because the hypothesis is often being
used as it was in the 1500s, as a premise to frame an
nerable to all of the charges originally posed against it,
that it is an instrument of bias.
It might be to avoid this last problem, that the
organizations shield themselves from the potential
hazard of an ill-founded premise by insisting that the
scientist know in advance that almost the entirety of
what he or she proposes has already been demon-
Clinical Chemistry 56:7 (2010)
performed. This situation is not only backwards, it is
stifling, as Gina Kolata pointed out in the New York
Times last year (19):
The institute’s reviewers choose such projects because, with
too little money to finance most proposals, they are timid
about taking chances on ones that might not succeed. The
make a major difference in cancer prevention and treatment
are all too often crowded out because they are too uncertain.
In fact, it has become lore among cancer researchers that
some game-changing discoveries involved projects deemed
too unlikely to succeed and were therefore denied federal
grants, forcing researchers to struggle mightily to continue.
The current granting system can be criticized as
being worse than contradictory—as being actually
incoherent—if on the one hand a preexisting hypoth-
esis is mandated as an experimental framework for a
grant application and then on the other hand grant
recognized, with all these layers of inculcated preexist-
tists seek to interrogate systems more comprehen-
sively. One example of a systems biology experiment is
a set of gene expression studies in which changes to
to a series of perturbations. Another type of systems
iments, in which, for example, scientists investigate
how the phosphorylation states of all proteins are al-
done to determine subtle epigenetic changes.
Such systems biology experiments cannot be use-
fully framed with a hypothesis aimed at falsification.
The requirement to do so can be comical and is cer-
tainly unhelpful to experimental design.
The NIH deals with this “problem” by labeling
such experiments as “hypothesis generating” experi-
ments (20); however, if a large systems biology exper-
this is proof that hypotheses are not required for big
esis to enter into an inquiry concerning a particular
sive arguments. The notion that the scientist does
not actually need to do the experiment to derive the
answer—that the scientist is so clever as to figure out
the answer in advance so that the experiment is simply
an act of confirmation—is quite seductive. After all,
one might fear that if all the scientist is doing is posing
a question and therefore is requiring nature to deliver
the answer, then what is the scientist other than a spe-
observer and recorder (and, in some special instances,
one who is especially insightful in deriving a principle
from recordings that can be shown to be predictive—
even if this last instance is noteworthy) with that of the
diviner, who can intuit the truth without the help of
nature to point the way. This is the seductive appeal of
century literature to see the folly of such an approach.
Hypotheses made without the help of nature are
doomed to failure, and the desire to prove such a con-
with a preacher than a scientist.
Indeed, some might argue that one does not need
to go back to the sixteenth century. The scientific liter-
ature of the twenty-first century has no shortage of hy-
to the variety of problems listed, not the least of which
helpful, disadvantageous, inconsistent, or unworkable
as instruments of either falsification or verification,
what should the scientist use as an alternative?
The way that science seems to be actually done
productively is to first conduct an experiment in re-
sponse to a question, rather than a hypothesis (Fig.
1). Why else would a scientist seek to experiment
and determine how nature works unless the scientist
had a question about how it works? A question func-
tions as an adequate framework for an experiment if
it is posed so that it can be answered with an exper-
The answer to a question might be a set of data,
and from these data the scientist can build a model. A
tested for its predictive, or inductive, power. Third, it
exists in a framework that accepts inductive reasoning.
not just falsified/rejected or affirmed/accepted in a
purely binary manner. Finally, a model can be said to
Clinical Chemistry 56:7 (2010)
be correct within a probability range. It does not have
to be absolutely correct, as long as the stated probabil-
ity is verifiable.
A query/model approach is also appropriate for
big science. For example, one can simply frame a pro-
the proteome change in response to X?” The resulting
data set can then be tested for its predictive power as a
model by asking whether the proteome changes the
same way in subsequent experiments (Fig. 1). From that
model of proteomic changes, one can determine the
turbations or settings by asking further questions, all
of science is actually done, which begs the question,
Why aren’t scientists more explicit about this?
It is time scientists embrace their role as induc-
tive agents. An exploration of the appropriate limits
and weaknesses of such a framework will afford the
scientist an appropriate way both to frame experi-
ments and to analyze data. A question as an initial
framework admits the scientist into new areas where
little is known, whereas the requirement for a pre-
proven hypothesis explicitly closes the door to ex-
ploring the unknown.
Having a question as the initial framework for a
project in advance of any experimentation also seems
to have a useful humbling effect on the scientist. The
act of posing a question forces the scientist to admit
that the answer is not yet known and that the question
therefore requires an experiment. From the act of do-
ing the experiment, one accumulates data that can be
used to build a model (Fig. 1). The model seems to be
the more appropriate framework for basing predic-
tions, because it is explicitly derived from experiential
data, and that experience can then be queried for its
inductive power. This approach seems an accurate de-
one can use large unknowns to pose big questions,
which demand novel and exciting experimentation.
For example, there should be no shame in posing a
question like, What is the cure to cancer? The greater
shame is the requirement that we claim to know the
requirement that will limit us to what we already think
search for an answer we do not yet have, freeing us to
see what we might discover.
Some might object that such a huge question does
large project and alerts the scientist to a great un-
known. Subsequent to such a large framework ques-
tion, the scientist can ask a more focused question,
such as, What genetic markers coassociate with sensi-
Fig. 1. Schematic representation of the question/model method.
An experimental project is first framed with a question. This question forces the design of an experiment to produce an answer,
which is a particular data set. The stability of the answer is determined by repetition. If the answer is stable, a model of reality
can be induced. This model is then tested for its predictive power. Any inaccuracy in the model can be corrected by perturbing
the model and then subjecting it to retesting.
Clinical Chemistry 56:7 (2010)
answers to these questions first would help in tailoring
medical care and, second, would point scientists to the
potential mediators of treatment resistance that still
require their attention. Once these mediators are dis-
covered, new mechanistic questions can be asked—all
in a particular focused program directed toward dis-
covery and innovation. This approach seems to be the
is called “scientific inquiry” and that at no stage re-
quires a hypothesis.
the intellectual content of this paper and have met the following 3 re-
quirements: (a) significant contributions to the conception and design,
acquisition of data, or analysis and interpretation of data; (b) drafting
or revising the article for intellectual content; and (c) final approval of
the published article.
Authors’ Disclosures of Potential Conflicts of Interest: No authors
declared any potential conflicts of interest.
Role of Sponsor: The funding organizations played no role in the
of data, or preparation or approval of manuscript.
Acknowledgments: I thank my colleagues at Novartis for their sup-
port, especially M. Fishman and B. Richardson, and the entire Mus-
for their critical reading and suggestions. Thanks to A. Abrams for
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