Steve M., Darby D.M., Geostatistics Explained - An Introductory Guide for Earth Scientists
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numerous test sites before being approved by the US Environmental
Protection Agency. All the results were consistent with the hypothesis, so
the general consensus at present is that “Apatite treatment reduced the
amount of lead available for leaching.” Nevertheless, the hypothesis may not
be correct or apply to all lead remediation sites, but, to date, there has been
no evidence to the contrary. Furthermore, the hypothesis is also supported
by sound scientific arguments for why the treatment works: the lead has a
divalent charge (Pb
2+
) and it substitutes readily for Ca
2+
in the apatite
(~Ca
5
(PO
4
)
3
(F,Cl,OH)) structure because it has a similar size and the
same charge.
4.7 Designing a “good” experiment
Designing a well-controlled, appropriately replicated and realistic experi-
ment has been described by some researchers as an “art.” It is not, but there
are often several different ways to test the same hypothesis, hence several
different experiments that could be done. Consequently, it is difficult to set a
guide to designing experiments beyond an awareness of the general princi-
ples discussed in this chapter.
Cost
of the
experiment
Very poor
Cost
Ability
Ability
to do the
experimen
t
Excellent
Qualit
y
of the ex
p
erimental desi
g
n
Figure 4.6 An example of the trade-off between the cost and ability to do an
experiment. As the quality of the experimental design increases, so does the
cost of the experiment (solid line), while the ability to do the experiment
decreases (dashed line). Your design usually has to be a compromise between
one that is practicable, affordable and of sufficient rigor. Its quality can be
anywhere along the X axis.
4.7 Designing a “good” experiment 43
4.7.1 Good design versus the ability to do the experiment
It has often been said “There is no such thing as a perfect experiment.” One
inherent problem is that as a design gets better and better, the cost in time
and equipment also increases, but the ability to actually do the experiment
decreases (Figure 4.6). An absolutely perfect design may be impossible to
carry out. Therefore, every researcher must choose a design that is “good
enough” but still practical. This trade-off is illustrated in Figure 4.6.The
quality of an experiment can be any point along the X axis and the “best”
compromise is not necessarily where the two lines cross – instead the
decision on design quality is in the hands of the researcher, and will be
eventually judged by their colleagues who examine any report from the work.
4.8 Conclusion
The above discussion only superficially covers some important aspects of
experimental design. Considering how easy it is to make a mistake, you
probably will not be surprised that a lot of published scientific papers have
serious flaws in design or interpretation that could have been avoided. Work
with major problems in the design of experiments is still being done and,
quite alarmingly, many researchers are not aware of these. As an example,
after teaching the material in this chapter, we often ask our students to find a
published paper, review and criticize the experimental design, and then offer
constructive suggestions for improvement. Many have later reported that it
was far easier to find a flawed paper than they expected.
4.9 Questions
(1) Give an example of confusing a correlation with causality.
(2) Name and give examples of two types of “apparent replication.”
44 Introductory concepts of experimental design
5 Doing science responsibly
and ethically
5.1 Introduction
By now you are likely to have a very clear idea about how science is done.
Science is the process of rational enquiry, which seeks explanations for
natural phenomena. Scientific method was discussed in a very prescriptive
way in Chapter 2 as the proposal of a hypothesis from which predictions are
made and tested by doing experiments. Depending on the results, which
may have to be analyzed statistically, the decision is made to either retain or
reject the hypothesis. This process of knowledge by disproof advances our
understanding of the natural world and seems impartial and hard to fault.
Unfortunately, this is not necessarily the case because science is done by
human beings who sometimes do not behave responsibly or ethically.
For example, some scientists fail to give credit to those who have helped
propose a new hypothesis. Others make up, change or delete results so their
hypothesis is not rejected, omit details to prevent the detection of poor
experimental design, and deal unfairly with the work of others. Most
scientists are not taught about responsible behavior and are supposed to
learn a code of conduct by example. Considering the number of cases of
scientific irresponsibility that have been exposed, this does not seem to be a
very good strategy. Thus, this chapter is about the importance of behaving
responsibly and ethically when doing science.
5.2 Dealing fairly with other people’s work
5.2.1 Plagiarism
Plagiarism is the theft and use of techniques, data, words or ideas without
appropriate acknowledgment. If you are using an experimental technique or
procedure devised by someone else, or data owned by another person, you
45
must acknowledge this. If you have been reading another person’s work, it is
easy to inadvertently use some of their phrases, but plagiarism is the repeated
and excessive use of text without acknowledgment. Once your work is pub-
lished, any detected plagiarism can affect your credibility and career. Quite
remarkably, we have detected plagiarism in manuscripts we have been asked to
review, including cases where material from the same journal has been copied.
5.2.2 Acknowledging previous work
Previous studies can be extremely valuable because they can add weight to a
hypothesis and even suggest other hypotheses to test. There is a surprising
tendency for scientists to fail to acknowledge previous published work by
others in the same area, sometimes to the extent that experiments done two or
three decades ago are repeated and presented as new findings. This can be an
honest mistake in that the researcher is unaware of previous work, but it is now
far easier to search the scientific literature than it used to be. When you submit
your work to a scientific journal for publication, it may be embarrassing to be
told that something similar has been done before. Even if a reviewer or the
editor of a journal does not notice, others may and are likely to say so in print.
5.2.3 Fair dealing
Some researchers cite the work done by others in the same field but down-
play or even distort it. Although it appears that previous work in the field
has been acknowledged because the publication is listed in the citations at
the back of the paper or report, the researcher has nevertheless been some-
what dishonest. We have found this in about five percent of the papers we
have reviewed, but it may be more common because it is quite hard to detect
unless you are very familiar with the work. Often the problem seems to arise
because the writer has only read the abstract of a paper, which can be
misleading. It is important to carefully read and critically evaluate previous
work in your field because it will improve the quality of your own research.
5.2.4 Acknowledging the input of others
Often hypotheses may arise from discussions with colleagues or with your
supervisor. This is an accepted aspect of how science is done. If, however,
46 Doing science responsibly and ethically
the discussion has been relatively one-sided in that someone has suggested a
useful and novel hypothesis to you, then you should seriously think about
acknowledgment. A colleague once said bitterly “My suggestions become
someone else’s original thoughts in a matter of seconds.” Acknowledgment
can be a mention (in a section headed “Acknowledgments”) at the end of a
report or paper, or you may even consider including the person as an author.
If you are ever in doubt as to which of these is appropriate, remember that
being generous and including someone as a coauthor (with his/her permis-
sion, of course) costs you very little, compared with the risk of alienating
them if they are omitted. Often asking the person what they think is appro-
priate will solve this problem.
It is not surprising that disputes often arise between supervisors and their
postgraduate students about authorship of papers. Some supervisors argue
that they have facilitated all of the student’s work by being the supervisor and
therefore expect their name to be included on all papers from the research.
Others recognize that single-authored papers may be important to the
student’s future, and thus do not insist on this. The decision depends on
the amount and type of input and rests with the principal author of the paper,
but it is often helpful to clarify the matter of authorship and acknowledgment
with your supervisor(s) at the start of a postgraduate program or new job.
5.3 Doing the sampling or the experiment
5.3.1 Approval
In some cases, you will need prior permission to undertake an experiment,
including submitting a risk assessment for an experimental procedure or field-
work that may expose you, or others, to potential hazards. If you are sampling in
a national park or reserve, you will need a permit. In both cases, you will have to
give a well-reasoned argument for doing the work, including its likely advan-
tages and disadvantages. In many countries and institutions, there are severe
penalties for breaches of permits or doing research without prior permission.
5.3.2 Ethics
Ethics are moral judgments where you have to decide if something is right or
wrong, so different scientists can have different ethical views. Ethical issues
5.3 Doing the sampling or the experiment 47
include honesty and fair dealing, but they also extend to whether experimen-
tal procedures can be justified. For example, some scientists think it is right to
test cosmetic products on animals such as rabbits or rats because it will reduce
the likelihood of harming or causing pain to humans, while others think it is
wrong because it may cause pain and suffering to the animals. Both groups of
scientists would probably be puzzled if someone said it was unethical to do
experiments on insects or plants. Similarly, some scientists believe it is wrong
to extract minerals or oil from areas of wilderness because of the potential for
damaging these ecosystems, while others believe the need to obtain these
resources is sufficient justification for extraction. Importantly, however, none
of these views can be considered the best or most appropriate, because ethical
standards are not absolute. Provided a person honestly believes, for any
reason, that it is right to do what he is doing, then he is behaving ethically
(Singer, 1992) and it is up to you to decide what is right. The remainder of this
section is about the ethical conduct of research, rather than whether a research
topic or procedure is considered ethical.
5.4 Evaluating and reporting results
Once you have the results of an experiment, then you need to analyze them
and discuss the results in terms of rejection or retention of your hypothesis.
Unfortunately, some scientists have been known to change the results of
experiments to make them consistent with their hypothesis, which is grossly
dishonest. We suspect this practice is more common than reported; it may
even be encouraged by assessment procedures in universities and colleges
where grades are given for the correct outcomes of practical experiments.
When we ask undergraduate students in our statistics classes if they have
ever altered their data to fit the expectations of their assignments, we tend to
get a lot of very guilty looks. We have also known researchers who were
dishonest. One had a regression line that was not statistically significant, so
they changed the data until it was. The second made up entire sets of data
for sampling on field trips that never occurred, and a third made up large
quantities of data for the results of laboratory analyses that were queried by
their supervisor because the data were “too good.” All were found out and
are no longer doing science.
It has been suggested that part of the problem stems from people becom-
ing attached to their hypotheses and believing they are true, which goes
48 Doing science responsibly and ethically
completely against science proceeding by disproof! Some researchers are
quite downcast when their results are inconsistent with their hypothesis.
However, you need to be impartial about the results of any experiment and
remember that a negative result is just as important as a positive one because
the understanding of the world has progressed in both cases.
Another cause of dishonesty is that scientists are often under extraordi-
nary pressure to provide evidence for a particular hypothesis. There are often
career (and financial) rewards for finding solutions to problems or suggest-
ing new models of natural processes. Competition among scientists for jobs,
promotion and recognition is intense and can also foster dishonesty.
The problem with scientific dishonesty is that the person has not reported
what is really occurring. Science aims to describe the real world, so if you fail
to reject a hypothesis when a result suggests you should, you will report a
false and misleading view of the process under investigation. Future hypoth-
eses and research based on these findings are likely to produce results
inconsistent with your findings. There have been some spectacular cases
where scientific dishonesty has been revealed, which have only served to
undermine the credibility of the scientific process.
5.4.1 Pressure from peers or superiors
Sometimes inexperienced, young or contract researchers have been pres-
sured by their superiors to falsify or give a misleading interpretation of their
results. It is far better to be honest than risk being associated with work that
may subsequently be shown to be flawed. One strategy for avoiding such
pressure is to keep good written records.
5.4.2 Record keeping
Some research groups, especially in industry, are so concerned about hon-
esty that they have a code of conduct: all researchers have to keep records of
their ideas, hypotheses, methods and results in a hard-bound laboratory
book with numbered pages that are signed and dated on a daily or weekly
basis by the researcher and supervisor. Not only can this be scrutinized if
there is any doubt about the work (including who thought of something
first), but it also encourages good data management and sequential record
keeping. Results kept on pieces of loose paper with no reference to the
5.4 Evaluating and reporting results 49
methods used can be quite hard to interpret when the work is written up for
publication.
5.5 Quality control in science
Publication in refereed journals ensures your work is scrutinized by at least
one referee who is a specialist in the research field. Nevertheless, this process
is more likely to detect obvious and inadvertent mistakes than deliberate
dishonesty and many journal editors have admitted that work they publish
is likely to be flawed (LaFollette, 1992). Institutional strategies for quality
control of the scientific process are becoming more common and many
have rules about the storage and scrutiny of data. At the same time,
however, there is a need in many institutions for explicit guidelines about
the penalties for misconduct, together with mechanisms for handling
alleged cases of misconduct reported by others. The responsibility for
doing good science is often left to the researcher. It applies to every aspect
of the scientific process, including devising logical hypotheses, doing well-
designed experiments and using and interpreting statistics appropriately,
together with honesty, responsible and ethical behavior, and fair dealing.
5.6 Questions
(1) A college lecturer said “For the course ‘Geostatistical Methods,’ the
grade a student gets for the exam has always been fairly similar to the
one they get for the assignment, give or take about 15%. I am a busy
person, so I will simply copy the assignment grades into the column
marked ‘exam’ on the spreadsheet and not bother to grade the exams at
all. It’s really fair of me, because students get stressed during the exam
anyway and may not perform as well as they should.” Please discuss.
(2) An environmental scientist said “I did a small pilot experiment with two
replicates in each treatment and got the result we hoped for. I didn’t
have time to do a bigger experiment but that didn’t matter – if you get
the result you want with a small experiment, the same thing will happen
if you run it with many more replicates. So when I published the result,
I said I used twelve replicates in each treatment.” Please comment
thoroughly and carefully on all aspects of this statement.
50 Doing science responsibly and ethically
6 Probability helps you make a
decision about your results
6.1 Introduction
Most science is comparative. Earth scientists often need to know if a
particular phenomenon has had an effect, or if there are differences in a
particular variable measured at several different locations. For example, what
is the permeability of sandstone with and without carbonate impurities?
How does turbidity vary across a glacial lake? How well does the distribution
of dew point temperature predict rainfall? But when you make these sorts of
comparisons, any differences among areas sampled or manipulative exper-
imental treatments may be real or they may simply be the sort of variation
that occurs by chance among samples from the same population.
Here is an example of commercial importance. Most diamonds are mined
from kimberlite deposits, which are volcanoes that have risen from great
depths in the Earth’s mantle at high speed. Sometimes, the kimberlite brings
along diamonds that have formed at high pressures and temperatures. But
not all kimberlites contain diamonds, and finding them within these rocks is
quite difficult.
Fortunately, many kimberlites contain large amounts of the mineral
garnet. A prospector noted that the garnets present in diamond-rich kim-
berlites were slightly darker than those in kimberlites lacking diamonds, and
subsequent research suggested that the change in color was caused by the
presence of small amounts of oxidized Fe, or Fe
3+
. To test if oxidized garnets
could be used to predict the presence of diamonds, the prospector collected
14 garnet samples: seven from diamond-bearing deposits of kimberlite, and
seven from kimberlite without diamonds (Table 6.1), and measured their
Fe
3+
content.
On average the %Fe
3+
content of garnets from diamond-bearing deposits
is 2.8% higher than those from diamond-free deposits, but by looking at the
51
data you can see that there is a lot of variation within both groups and even
some overlap between them.
Even so, the prospector might conclude that diamonds are associated
with oxidized (Fe
3+
-bearing) garnets. But there is a problem. How do you
know that this difference between groups is meaningful or significant?
Perhaps it simply occurred by chance and the oxidation state of the garnet is
not a good predictor? Somehow you need a way of helping you make a
decision about your results.
Even when there may seem to be a sound scientificexplanationfor
the phenomenon you observe, statistics can be very useful in making a decision
about your results. The need to make such decisions led to the development
of tests that provide a commonly agreed-upon level of statistical significance.
6.2 Statistical tests and significance levels
Statistical tests are just a way of working out the probability of obtaining
the observed, or an even more extreme, difference among samples (or
between an observed and expected value) if a specific hypothesis (usually
the null of no difference) is true. Once the probability is known, the
experimenter can make a decision about the difference, using criteria that
are uniformly used and understood. Here is a very easy example where the
probability of every possible outcome can be calculated.
Imagine you visit the beach after a big storm, and notice that the usually
white sand has now turned gray. What has happened is that many of the
Table 6.1 The %Fe
3+
content of garnets with and without
coexisting diamonds.
Sample Without diamond With diamond
1 1.0 1.5
2 0.5 3.3
3 0.7 5.8
4 2.3 3.2
5 1.1 6.7
6 0.8 4.2
7 1.4 2.5
Average 1.1 3.9
52 Probability helps you make a decision about your results