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Two concepts deserve mention here: ‘Inference’ and ‘Acceptance’. ‘Inference’ implies greater or lesser strength of fact. It is usually expressed as a probability of a given result being observed. If there is a high probability that the result which you have observed is associated with the hypothesis being true, we talk about ‘strong’ evidence. If the observed outcome is little different from what we would expect to see if the null hypothesis were true, we talk about ‘weak’ or ‘no’ evidence.
At some point, we need to make a decision about whether to accept or reject the null hypothesis, that is, to make a statement about whether or not we believe that the hypothesis is true. ‘Acceptance’ implies a cut‐off point upon which action will be taken. We will discuss cut‐off points in Chapter 5. It is important not to confuse political expediency (acceptance) with scientific validity (inference).
1.5 NEXT STEPS
Every year, at least one student shows up at my door, holds out an open notebook with a page full of numbers, and says, ‘I’ve collected all this data9 and now I don’t know what to do with it’. I strongly resist the temptation to tell them to go away, or to ask why they didn’t come to see me months ago. I usher them in and see what we can salvage. Usually, it is a debacle. The data collected are not suitable for testing the hypothesis; their sample is poorly defined; they don’t have enough of the right types of observations; they have used different methods for collecting data at baseline and follow‐up; the list goes on and on.
Box 1.3 summarizes the steps that should be undertaken when conducting research. Although Steps 1 and 2 are essential (‘Getting the question right’), probably the most important step is Step 3, the point at which you design the research project. It is vital at this stage that you consult a statistician (as well as others who have done similar research). Be prepared to accept that your hypothesis may need modifying, and that the design that you first thought of is not perfect and would benefit from improvements. It is very unlikely that you will have got it right at your first attempt. Be prepared to listen and to learn from your mistakes. As I said in the Introduction to this book, statisticians may be perceived as monstrous, inhuman creatures intent only on humiliating those who come to consult them. In reality, the statistician is there to advise you concerning the likelihood of being able to prove your hypothesis, guide you in the design of the study, the choice of measurements which you intend to make, and the type of analyses you plan to undertake. Months or years of effort can be wasted if you embark on a study which is flawed in its design. Do not take the chance! Be brave! Be thick‐skinned! Talk with statisticians and accept their advice. Even get a second opinion if you feel very uncertain about the advice you are given.
1.6 RESEARCH DESIGN
There is a wide variety of research designs which can be used to address the many research questions that you are likely to ask. There is no strictly right or wrong answer concerning which design to use. You should recognize, however, that some designs are stronger when it comes to arguing the truth of your hypothesis. The aim in carrying out any research will always be to obtain the maximum information from a given design in relation to a particular research question, given the time and financial resources that are available.
1.6.1 Project Aims
Coming up with an interesting and useful research question will always involve reading the relevant literature (both books and journals) to explore how other people have tackled similar problems, and discussing with colleagues how best to investigate the problem at hand. Once you have done that, you can think about what it is you want to achieve in your research.
Projects have different aims. An undergraduate student project with 15 subjects carried out part‐time over two months may not have much chance of establishing new findings that are statistically significant, but it will introduce the student to hypothesis formulation, designing a study, writing a research protocol, sampling, subject recruitment, data entry, computer analysis, and writing up the results. On the other hand, an MSc project carried out full‐time over four months will require the same skills as the undergraduate project, but will usually involve a more detailed consideration of design, sample size, and study power (see Chapter 12). It will also provide an opportunity to write a detailed report and to make a presentation of the findings (both for assessment), usually to an audience of postgraduate peers and their tutors. More demanding undergraduate projects may include some or all of these additional elements. For a PhD, or for funded research, all of these elements will be present, plus the requirement to write paper(s) for submission to a peer‐reviewed journal and to present findings to a public audience at scientific meetings. As a professor of mine once said, ‘If you haven’t published the paper, you haven’t done the work’.
BOX 1.3 Steps in undertaking research
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•Step 1. Make observations about the world. Science doesn't happen in a vacuum. |
| •Step 2. Construct a Hypothesis. State clearly the aims and objectives of your study. Formulate the Null Hypothesis. | |
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Formulate the Null Hypothesis. |
| •Step 3. Design the experiment. | |
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This is the stage at which you should seek the advice of a statistician
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regarding the hypothesis, sample selection, sample size, choice of measurements, and the type of analyses and statistical tests to be used. Failure to consult properly at this stage may mean that any work that you do may be a waste of time. Do not take that chance! |
| •Step 4. Conduct the research. | |
| •Step 5. Analyze the data both observationally (do the numbers make sense?) and statistically. | |
| •Step 6. Interpret the results (draw inferences) and write your report (for marking or for publication). Work that is not marked or published may just as well never have been completed. | |
| •Step 7.Bask in the glory of a job well done. |
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1.6.2 Demonstrating Causality
The underlying purpose of most research is to find evidence in support