You don't have to agree with all of my chosen quotations, and in this instance I disagree with my former supervisor. At the least, statistical treatment of data can help to convince yourself, and perhaps others too, of the reliability of your conclusions. Statistics are also important in cricket and bridge.

Often one repeats an estimation (notably of an enzymic activity) several times, and then reports the average result (the mean) and its standard deviation (that is, an indicator of how much the results scatter about the average). This is a descriptive use of statistics. A predictive use occurs when assessing the probability that two means (such as the activities of an enzyme in organisms grown under two conditions) are really different. In this chapter we shall see how to work out a standard deviation, and how to compare means. Going beyond these procedures (which are not all that simple) soon leads to a call for the services of a professional statistician, and damn the expense! It's like going to law.

Even determining the mean and its standard deviation is not straightforward because we have to take into account whether a mean is calculated from a whole population or from a sample of a population, and also whether the total number of individual values that contribute to a mean are small (in practice less than 30) or large (≥30).

The object of this chapter is to encourage you to think whether or not your answer to a problem looks reasonable or ridiculous. In general, a reasonable answer is likely to be a right answer. An answer that looks ridiculous might also be right, but you should then be alert to check your calculation very carefully. Of course, there will be times when you do not know what to make of an answer – is it reasonable or is it not? The better your background of knowledge and experience, the less often will this uncertainty happen.

A ridiculous answer that is wrong

Here is the problem: calculate what dry weight of bacteria will be present in 10 litres of medium in a fermenter after 10 h when at time zero there are 10 organisms ml−1 and there is a lag of 1 h before exponential growth (doubling time 20 min) begins. One organism has a dry weight of 1 × 10−12 g.

Data-handling means the interpreting and refining of experimental results. This book is aimed at helping to improve confidence and skill in data-handling. It is intended for undergraduate students, and for graduate students who may still have a little to learn.

Although microbiology began with simple observations (the organisms are small, they have various shapes, and some are motile) the subject has become a quantitative, experimental science. As an example consider the following statement:

To reach this conclusion one must grow the organisms in a certain way, make weighings, extract the polymer, do a chemical assay, and then put all the information together in its proper order. None of these practical steps is difficult, but to achieve the final result, clarity of thought, rather than great mathematical ability, is definitely needed.

In recent times, the words ‘data-handling’ have taken on a second meaning, that is, the manipulation of very large quantities of data (such as DNA sequences) by using computer programs for analysis and comparison. This new big area of database management is not covered here. Everything in this book can be done with pencil and paper and a pocket calculator. That is by no means to decry computers; the whole of this text was written on a word processor, and all the figures have been drawn with Excel® or Corel Draw®.

Science is a curious profession. It is relatively easy to get into, but much harder to be truly successful at. There are many different paths to success and just as many ways to fail. Many who have an interest in science in school never find their way into a science career. Many who do get an advanced degree in science are never able to get a grant or conduct a successful research program and may leave the field after a while. Some are tempted to cut corners and thus ruin their careers. Even those who have a science job may not be secure in their abilities or their productivity.

This book has four parts. In Chapter 1, science as a career is explored. What do scientists in different fields study? What skills are needed? How do scientists spend their time? How do you choose the right career path? Chapters 2 and 3 cover the ins and outs of creativity and problem solving, the central keys to success in research. Chapter 4 discusses the social dimension of being a scientist.

The most difficult part of a scientist's job is conducting research. Huge amounts of time are wasted in science experiments that fail, ideas that don't pan out, and papers that are never finished. Effort is wasted on proposals that don't get funded and projects whose results are never published. Even published studies are often flawed. Why?

Science is not merely conceptual, but instead involves active manipulation of the world to arrive at new knowledge. In this chapter, the implications of the ideas presented in the first chapters are explored. First, the process of developing and executing a scientific research program are explored, publishing papers is discussed, some ethical issues are touched on, and finally, the problem of interfacing science with the world of policy is addressed.

CONDUCTING A RESEARCH PROGRAM

Research that is random and aimless is unlikely to be very productive. In the age of naturalists, it was productive to collect specimens and observe animals in the wild because little or nothing was known. But today, any random observation is likely to be already known. Thus a key to success is to focus on a problem.

Earlier chapters discussed concepts that can help one to identify a problem worth working on. To review briefly: paradox can help one identify an opportunity; new tools and methods can open up topics to analysis; obtaining a larger data set or one at a larger scale than previous studies can lead to insights; and refining or testing a theory is always a productive avenue. Of course the idea of leverage as embodied in the Medawar zone can help one pick topics with a high payoff.

Given that a promising topic has been picked, the textbook view and the impression given to graduate students is that you should next design and conduct an experiment or study.

Science as a career is fascinating and fulfilling. The popular picture of what scientists do, however, does not provide a useful guide to those considering a science career. The first section of this book provided an overview of science as a profession and what scientists do. The central focus of the book, however, is on the inner game of science. How do scientists solve problems and discover new things? How can they increase productivity? What techniques can they use? To address this issue, a strategic problem solving approach is proposed.

Strategic thinking is an overall technology for problem solving with the potential for increasing both the quantity and quality of work in fields where creation of novel solutions is critical, such as design, invention, institutional innovation, research, software development, and engineering. It is based on an understanding of both outward processes (actions) and inner operations (cognition, attitudes). It takes particular care to be alert to the inherent flaws in all aspects of the problem solving process, and builds in checks and balances against these flaws.

There are four factors involved in the successful development of an innovative creation: creative mental functioning, an understanding of the discovery process, an appreciation of the structure of multistep problems, and the utilization of reality checks. Creative mental functioning is necessary to be able to recognize problems, overcome intimidation by the status quo, overcome barriers to thought, and avoid stagnation.

The second chapter of this book presented the techniques of strategic thinking in terms of the nature of problem solving, how discoveries are made and tested, how creativity can be harnessed and enhanced, and how attitude and style affect strategic problem solving effectiveness. This provides the foundation for actual strategic problem solving in terms of the mastery of one's cognitive tools and abilities. Such an understanding is essential to success, but there is more to it than this. There are characteristics of the problems themselves that one must also get a feel for before even the best strategist can be successful. To make an analogy, it is not sufficient for the sculptor to have an aesthetic vision, to know the symbolism he wishes to convey, and to have harnessed his mental faculties to the task, it is also essential that he understand the nature of his materials: the way that different woods respond to carving and polishing, the receptivity of these woods to stain, the susceptibility of different types of stone to cracking, and so on. That is, one must know something about the subject to be strategized about per se. This is usually considered domain-specific knowledge (of cars or diseases, of pizza or electronics) which of course is beyond the scope of this book because there are hundreds of domains and their intersections where people are faced with problems.

Science is about mathematics and rigor, but the human mind is sloppy and vague. We are prone to jumping to conclusions, prefer short chains of logic, are easily duped by optical illusions (even when told they are illusions beforehand), and are influenced by group think. This section is about the inner game of science, the mental world where discovery and proof take place. This is the terra incognita that few scientists have thought about, but about which they remain ignorant at their peril. A strategic approach to problem solving is presented and applied to help elucidate the solution to the paradox of how our illogical mind can ever produce reliable, logical results.

Because scientific discovery is inherently about open-ended, complex problems, it is not really possible to apply a cookbook approach (“the scientific method”). Rather, I suggest that a strategic approach to problem solving is a more effective approach. For those who are not born strategists, this involves tuning up one's thinking machinery specifically for this type of problem. Specifically, there are three steps to creating successful novel products. First, one must be capable of generating novel (but useful) ideas, of overcoming routine ways of functioning, and of putting together information in new ways. Without this capacity, one is limited to solving problems defined by others (the definition of a drone). Second, one must be able to use this creative capacity to discover/invent something new.

At one time, there were over 250 different styles of reference in the scientific literature (Garfield 1986). The editors of some major biomedical journals therefore had good reason to convene in Vancouver, Canada, in January 1978, to work out a uniform reference style. One of their suggestions was that authors should number references in the order in which they appear in the text (International Committee of Medical Journal Editors 1997).

Vancouver versus Harvard style?

Although many of the major journals in the biomedical field have adopted the Vancouver style, some still prefer the Harvard system (first used in 1881 by a zoologist at Harvard University [Chernin 1988]) in which the author's name and the year of publication are cited in the text. In the fictive sentence below, I have mixed the two styles to illustrate their differences:

Most readers prefer the Harvard system because they like to know just what author is being cited as they read the text. Still, the name-and-year system does have disadvantages: difficulty for readers who see an interesting item in the reference list in locating that reference in the main text; and, more important, the disruption of the text when a large number of references need to be cited within a paragraph, as in this example (Bengtsson 1968):

Abbreviations should be kept to a minimum. So a formulation such as the following is not to be recommended (quoted from Spiers 1984):

It requires some years in the profession to grasp immediately that this patient with atherosclerotic heart disease and a history of myo-cardial infarction, status post-coronary-artery-bypass graft, had an episode of bright red blood per rectum prior to admission for endoscopic retrograde choledochopancreatography!

The abbreviations used in this sentence are probably all accepted in the specialty. But just because an abbreviation is permitted does not mean that you are obliged to use it.

So when should you consider using an abbreviation? Let us take an example. The term nonsteroidal anti-inflammatory drug (accepted abbreviation NSAID) may not warrant abbreviation unless it occurs, say, a dozen times in a paper of standard length.

Some abbreviations are more readily understood than the full forms: DNA, AIDS, laser. Often such abbreviations are accepted in the major bibliographic databases. If so, you are free to use them without definition – even in the title and the abstract section.