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In a typical thesis (or research paper), data and argument are used to build a case. That is, a logical narrative is used to persuade the reader that the claims of the thesis are reasonable and are supported by evidence. From this perspective, maybe half of a thesis can be viewed as a sequence of three components: first, how the data was gathered and what it is intended to represent; second, what the gathered data looks like; third, how it should be interpreted. How to present ‘what the gathered data looks like’ is the subject of this chapter.
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A note on terminology. Discussion of how to present results is clouded by the inconsistencies in the way experiments and their outcomes are described. In many fields of research, for example, data is the outcome of the recording of measurements. The data could have been recorded by you as the researcher using the instruments you devised to test your hypotheses, or recorded by some other researcher and then made available. Or they could have been recorded for some other purpose, such as the temperatures recorded at a meteorological station, or the share prices recorded at a stock exchange. But data can also be the subject of an experiment. A researcher investigating a weather model could use temperature measurements as an input, and the recorded values—‘data’ in the above definition—could be the input to the model, which also produces ‘data’ as output. Here I use data to describe experimental results, or measurements, and outcomes or results to describe what the researcher found by interpreting these measurements.
When faced with doing the analysis, Don began to made some fresh judgments as to whether something should be included; an obvious risk in making judgments during the analysis is that subconsciously he might exclude cases because they did not fit his hypothesis, and initially he did make errors of this kind. What it also reflected was that his thinking had developed, and he needed to adjust his early chapters accordingly.
Almost certainly because of one of the classic fallacies of science. If you repeat an experiment with small variations until it succeeds, and then stop, it may seem as though the desired outcome is achieved; if there is any kind of uncertainty in the experiment, it is just as likely that the positive outcome occurred by chance. If this success is all that is reported, the reader gets a highly distorted view of the true results. This is known as confirmation bias.
In some attacks on science, certain kinds of knowledge are condemned as ‘only a theory’. People who use such arguments have failed to realize that the mathematical constructs that are used to design planes or transmit electricity are, also, ‘only theories’. We may not be certain of their truth, but they are the knowledge for which we have the most consistent evidence.
- Outcomes and Results
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