SOL·LICITA:

It is important to differentiate between:

z assessing the adequacy of the research hypothesis which underlies the research study in question;

z testing the null hypothesis and alternate hypothesis in significance testing (or statistical inference) as part of the statistical analysis.

These are frequently confused. The hypothesis testing model in statistical analysis deals with a very simple question: are the trends found in the data simply the consequence of chance fluctuations due to sampling? Statistical analysis in psychology is guided by the Neyman–Pearson hypothesis testing model although it is rarely referred to as such and seems to be frequently just taken for granted. This approach had its origins in the 1930s. In the Neyman–

Pearson hypothesis testing model there are two statistical hypotheses offered:

z That there is no relationship between the two variables that we are investigating – this is known as the null hypothesis.

z That there is a relationship between the two variables – this is known as the alternate hypothesis.

The researcher is required to choose between the null hypothesis and the alternate hypothesis. They must accept one of them and reject the other. Since we are dealing with probabilities, we do not say that we have proven the hypothesis or null hypothesis. In effect, hypothesis testing

assesses the hypothesis that any trends in the data may be reasonably explained by chance due to using samples of cases rather than all of the cases. The alternative is that the relationship found in the data represents a substantial trend which is not reasonably accountable for on the basis of chance.

To put it directly, statistical testing is only one aspect of hypothesis testing. We test research hypotheses in other ways in addition to statistically. There may be alternative explanations of our findings which perhaps fit the data even better, there may be methodological flaws in the research that statistical analysis is not intended to, and cannot, identify, or there may be evidence that the hypo-theses work only with certain groups of participants, for example. So significance testing is only a minimal test of a hypothesis – there are many more considerations when properly assessing the adequacy of our research hypothesis.

Similarly, the question of direction of a hypothesis comes up in a very different way in statistical analysis. Once again, one should not confuse direction when applied to a research hypothesis with direction when applied to statistical signi-ficance testing. One-tailed testing and two-tailed testing are discussed in virtually any statistics textbook (for example, Chapter 17 of our companion statistics text Introduction to Statistics in Psychology (Howitt and Cramer, 2011a) is devoted to this topic). Quite simply, one-tailed testing is testing a directional hypothesis whereas two-tailed testing is for testing non-directional hypotheses. However, there are exacting requirements which need to be met before applying one-tailed testing to a statistical analysis:

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z There should be very strong theoretical or empirical reasons for expecting a particular relationship between two variables.

z The decision about the nature of the relationship between the two variables should be made in ignorance of the data. That is, you do not check the data first to see which direction the data are going in – that would be tantamount to cheating.

z Neither should you try a one-tail test of significance first and then try the two-tail test of significance in its place if the trend is in the incorrect direction.

These requirements are so demanding that very little research can justify the use of one-tailed testing. Psycho-logical theory is seldom so well developed that it can make precise enough predictions about outcomes of new research, for example. Previous research in psychology has a tendency to manifest very varied outcomes. It is notorious that there is often inconsistency between the outcomes of

ostensibly similar studies in psychology. Hence, the difficulty of making precise enough predictions to warrant the use of one-tail tests.

One-tailed (directional) significance testing will produce statistically significant findings more readily than two-tailed testing – so long as the outcome is in the predicted direction.

Hence the need for caution about its incorrect use since we are applying a less stringent test if these requirements are violated. Two-tailed testing should be the preferred method in all but the most exceptional circumstances as described above. The criteria for one- and two-tailed two types of significance are presented in Figure 2.2.

The distinction between a research hypothesis (which is evaluated in a multitude of ways) and a statistical hypo-thesis (which can be evaluated statistically only through significance testing) is very important. Any researcher who evaluates the worth of a research hypothesis merely on the basis of statistical hypothesis testing has only partially completed the task.

FIGURE 2.2 The circumstances in which to use one- and two-tailed tests of significance

Figure 2.3 summarises the four possible types of hypothesis which can be generated by considering the causal versus non-causal and directional versus non-directional distinc-tions. The letters A and B refer to the two variables. So A could be attitude similarity and B interpersonal attraction.

It should be stressed that without a rationale for a hypothesis based on theory or previous research, the case for examining the relationship between two variables is weakened. Consequently, consideration should be given to other reasons for justifying researching the relationship between two variables. Given the billions of potential variables that could be available to psychologists, why choose variable 2 743 322 and variable 99 634 187 for study? Research is not about data collection and analysis for its own sake. Research is part of a systematic and coordinated attempt to under-stand its subject matter. Until one underunder-stands the relationship between research and advancement of understanding, research methods will probably remain a mass of buzzing confusion.

The aims and hypotheses of a study are its driving force. Once the aims and hypotheses are clarified, other aspects of the research fall into place much more easily. They help focus the reading of the published literature on pertinent aspects since the aims and hypotheses help indicate what is most relevant in what we are reading. Once the past research and writings relevant to the new research study have been identified with the help of clear aims and hypotheses, the introduction can be written using more convincing and coherent justifications for them. The aims and hypotheses clarify what variables will need to be measured. Similarly, the aims and hypotheses help guide the researcher towards appropriate research design. The data will support or not support the hypotheses, either wholly or partially. Finally, the discussion of the results will primarily refer back to the aims and hypotheses. It is hardly surprising, then, to find that the aims and hypotheses of a study can be the lynchpin that holds a report together.

If they are incoherent and confused then little hope can be offered about the value of the study.

FIGURE 2.3 The four different types of hypotheses according to directionality and causality

*An alternative would be to predict ‘less’.

2.6 Difficulties in formulating aims and hypotheses

The aims or objectives of published studies are usually well defined and clear. They are, after all, the final stage of the research process – publication. It is far more difficult to be confident about the aims and hypotheses of a study that you are planning for yourself.

One obvious reason for this is that you are at the start of the research process. Refining one’s crude ideas for research into aims and hypotheses is not easy – there is a lot of reading, discussing, planning and other work to be done. You will usually have a rough idea of what it is that you want to do but you are not likely to think explicitly in terms of aims and hypotheses – you probably have little experience after all. Take some comfort in personal construct theory (Kelly, 1955) which suggests that humans act like scientists and construct theories about people and the nature of the world. You may recognise yourself behaving like this when you catch yourself thinking in ways such as

‘if this happens, then that should happen’. For example, ‘if I send a text message then he might invite me to his party in return’.

This kind of statement is not different from saying ‘if someone has the same attitude as someone else, then they will be attracted to that person’ or ‘the more similar someone’s attitude is to that of another person, the more they will be attracted to that individual’.

These are known as conditional propositions and are clearly not dissimilar from hypotheses. This kind of thinking is not always easy to recognise. Take, for example, the belief or statement that behaviour is determined by one’s genes. At first sight this may not appear to be a conditional or ‘if . . . , then . . .’ proposition. However, it can be turned into one if we restate it as ‘if someone has the same genes as another person, they will behave in the same way’ or ‘the more similar the genes of people are, the more similar they will behave’.

There is another fundamental thing about developing aims and hypotheses for psy-chological research. If people are natural scientists testing out theories and hypotheses, they also need to have a natural curiosity about people and the world. In other words, research ideas will only come to those interested in other people and society. Research can effectively be built on your interests and ideas just so long as you remember that these must be integrated with what others have done starting with similar interests.