The following are examples of untestable/non-falsifiable hypotheses:

In case-control studies, other options exist to avoid biases. Interviewers, study staff and study participants need not be aware of the precise hypothesis under study. If they do not know the association being tested, they are less likely to try to provide the expected answer. Keeping study personnel in the dark as to the research hypothesis is in fact often very impractical. The interviewer will almost always know the exposures of greatest potential interest as well as who is a case and who is a control. We therefore have to rely on their honesty and also on their training in basic research methodology, which should be a part of their professional background; objectivity is the hallmark at all stages in science.

Aptitude-Treatment Interaction as a framework on individual differences in learning.

The rationale of hypothesis testing could be explained in the perspective of Principle of Falsification,introduced by prominent philosopher of science, Karl Popper (1959).According to Popper, conclusive verification of hypotheses is notpossible, but conclusive falsification is possible. The validity ofknowledge is tied to the probability of falsification. For example, avery broad and general statement such as "Humans should respect andlove each other" can never be wrong and thus does not bring us anyinsightful knowledge. The more specific a statement is, the higher thepossibility that the statement can be negated. If the statement has ahigh possibility of falsification and can stand "the trial of fire,"then we can confirm its validity.

If yes, in favour of what alternative hypothesis?

Key words: aptitude-treatment interaction, decision theory, criterion-referenced measurement.

For each case of liver cancer, two controls are drawn in order to achieve greater statistical power. (One could draw even more controls, but available funds may be a limiting factor. If funds were not limited, perhaps as many as four controls would be optimal. Beyond four, the law of diminishing returns applies.) After obtaining appropriate permission from data protection authorities, the cases and controls, or their close relatives, are approached, usually by means of a mailed questionnaire, asking for a detailed occupational history with special emphasis on a chronological list of the names of all employers, the departments of work, the job tasks in different employment, and the period of employment in each respective task. These data can be obtained from relatives with some difficulty; however, specific chemicals or trade names usually are not well recalled by relatives. The questionnaire also should include questions on possible confounding data, such as alcohol use, exposure to foodstuffs containing aflatoxins, and hepatitis B and C infection. In order to obtain a sufficiently high response rate, two reminders are sent to non-respondents at three-week intervals. This usually results in a final response rate in excess of 70%. The occupational history is then reviewed by an industrial hygienist, without knowledge of the respondent’s case or control status, and exposure is classified into high, medium, low, none, and unknown exposure to solvents. The ten years of exposure immediately preceding the cancer diagnosis are disregarded, because it is not biologically plausible that initiator-type carcinogens can be the cause of the cancer if the latency time is that short (although promoters, in fact, could). At this stage it is also possible to differentiate between different types of solvent exposure. Because a complete employment history has been given, it is also possible to explore other exposures, although the initial study hypothesis did not include these. Odds ratios can then be computed for exposure to any solvent, specific solvents, solvent mixtures, different categories of exposure intensity, and for different time windows in relation to cancer diagnosis. It is advisable to exclude from analysis those with unknown exposure.