Homework for my TA – week 11.
December 9, 2011, 2:21 am
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Is it possible to prove a research hypothesis?
December 8, 2011, 12:42 am
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A hypothesis is a testable prediction of what you think the results of a research study are likely to be. It is a statement about the relationship between two or more variables. In statistics, the only way of supporting your hypothesis is to refute the null hypothesis.

A null hypothesis is a working hypothesis that is to be disproved by a statistical test in favour of the alternative hypothesis.  Rather than trying to ‘prove’ your idea (the alternate hypothesis) right you must show that the null hypothesis is likely to be wrong – you have to ‘refute’ or ‘nullify’ the null hypothesis. You have to assume that your alternate hypothesis is wrong until you find evidence to the contrary.

Karl Popper said, ‘All swans are white cannot be proved true by any number of observations of white swan – we might have failed to spot a black swan somewhere – but it can be shown false by a single authentic sighting of a black swan. Scientific theories of this universal form, therefore, can never be conclusively verified, though it may be possible to falsify them.’

Popper’s idea about doing science is that you formulate a hypothesis, try to prove it wrong, and, from your results, formulate a new hypothesis. Why not try to prove it right?  Because you can’t; you never know if there isn’t one more experiment that will prove it wrong.

Einstein said ‘A thousand scientists can’t prove me right, but one can prove me wrong’.  We can’t prove a hypothesis but we can disprove it.

It is easier to disprove a hypothesis – it would take just one observation to refute the hypothesis, than it is to prove a hypothesis – it is impossible to test every possible outcome.

Science advances only through disproof.

Absolutely proving a hypothesis is impossible. As to prove something implies it can never be wrong.  However, well-designed scientific experiments can allow researchers to strongly infer from empirical evidence that their hypothesis is correct.

There is no ‘proof’ or absolute ‘truth’ in science.



Homework for my TA – comments that I have made. 25/11/11
November 25, 2011, 11:41 pm
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Do the ends justify the means?
November 21, 2011, 11:29 pm
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Some of our greatest findings have come from very controversial studies where standards of ethical practice have been questionable. So why do we need and follow ethical guidelines when all that they do is restrict what we can and cannot do and slow up the process of discovery and development.  Doesn’t most research rely on a little bit of deception?  If all information is disclosed to participants it may undermine the purpose of the study.  If all information is not disclosed then participants are not giving informed consent.  In the case of harm, should the discomfort of the few condemn influential and positive research findings?

Milgram’s study of obedience (1963), demonstrated that many people are susceptible to manipulation by those in positions of authority and were capable of committing heinous acts.


Zimbardo’s Stanford Prison experiment (1973), demonstrated how people conform to the social roles they are expected to play, especially if the roles are strongly stereotyped and that the roles that people play can shape their behaviour and attitudes.


Although the above studies have greatly attributed to our knowledge and understanding they have been heavily criticised for breaching ethical standards.

Other studies have gone far beyond an acceptable level of ethical standards with human beings treated in the most depraved and appalling manner.

The Tuskegee syphilis experiment (1932 –1972) was a research project intended to document the natural progression of syphilis. The subjects of this study did not have a meaningful understanding of their condition or the nature of the research that was being conducted.  Many subjects thought they were receiving beneficial medical care and did not understand they were participating in research designed to specifically observe the course of their illness. The subjects were followed, untreated, many years after penicillin was known to cure syphilis. Physicians deliberately denied these men treatment for syphilis and also attempted to prevent treatment from other sources.


The Jewish Chronic Disease Hospital (1963) performed experiments on chronically ill, mostly demented patients.  The subjects of this study who did not have cancer were injected with live human cancer cell into their bloodstream. The purpose of the research was to determine how a weakened immune system influenced the spread of cancer. The physicians did not inform the patients as to what they were doing, rationalising their actions as they did not want to scare the patients and they thought the cells would be rejected.


In 1948 In the Nuremberg Code was developed in response of judgment by an American military war crimes tribunal conducting proceedings against 23 Nazi physicians and administrators for their willing participation in war crimes and crimes against humanity. Nazi physicians had conducted medical experiments on concentration camp prisoners who died or were permanently affected as a result.  The Nuremberg Code laid down 10 standards for physicians to conform to when carrying out experiments on human participants.


In 1964, the World Medical Association developed ethical principles as guidance for medical doctors in biomedical research involving human subjects. The World Medical Association adopted the Declaration of Helsinki in response to concerns with research on patient populations. The Declaration of Helsinki has been regularly revised.


For the American psychological Asociation (APA) guidelines –


For the British Psychology Society guidelines –


All psychologists are bound by these codes of ethics, for which I am thankful, as they guard against conducting research which may be detrimental to another human being, which may cause feelings of guilt.  I would not ask someone to do something that I myself would not do.  Ethical guidelines give protection for the participant and the researcher.

Week 4: homework for my TA, comments I’ve made.
October 28, 2011, 7:41 pm
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“Is it dishonest to remove outliers and / or transform data?”
October 18, 2011, 10:02 pm
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An outlier is a data point that is far outside the norm of other values in a random sample from a population. If most of the data fit a particular trend an outlier is a data point that is radically outside of that trend, a point far away from the line of fit. An outlier is a unusually large or an unusually small value in comparison to others data points.

My initial thoughts on the removal of data was that it was cheating, but after considering how outliers are caused I can see that most are not valid data points and do not add anything to a study. Outliers make statistical analyses difficult, and can distort the interpretation of the data, influencing the mean and the variability, standard deviation and consequently the findings of a study.

An outlier maybe the result of an error in measurement, such as a human error in data collection, recording or entry. These errors may be corrected using the original data, double checking and recalculating, but if they cannot be corrected they should be removed as they do not represent valid data points.

Another cause could be a participant’s misinterpretation of the task, and their interpretation leads them to perform the task wrongly, in a different manner to all other participants, so their data is not a fair assessment of the participant performance of the same task, therefore their data is not valid. Participant may chose to behave in certain ways, such as purposely giving false, invalid data to appear socially acceptable. For example studies investigating sexual experience, educational achievement, the rate of truancy or financial income. Individual participant effects, such as enduring high levels of stress on the day of the test, illness or fatigue or perhaps immediate environmental effects such as a distracting noise outside the testing lab, can effect results. Participants may become bored with a task and answer any old how resulting again in data that is not valid.

Other causes of outliers are researcher effects, an attractive researcher may affect a participant’s answers or multiple researchers may record data in different ways. A participant may gather the true nature of the study and the desired outcome and may adjust their answers in accordance just to please or to oppose.

Outliers can also occur due an error in sampling. For example studying nurses and their income, some of the ward sisters with a considerable higher income could be mistakenly including in the sample. These could provide undesirable outliers, which should be removed as they do not reflect the target population.

Incorrect assumptions about the distribution of the data can also lead to outliers. Data may not fit the original assumption and may be affected by unanticipated long or short-term trends. For example, a study of library usage rates for the month of September finds outlying values at the beginning, low rates and end of the month, high rates. This data may have a legitimate place in the data set as it may reflect the return of students for the new semester, the low rate and the run up to midterm exams, the high rate.

An outlier can come from the population being sampled legitimately through random chance. Sample size is important in the probability of outlying values. Within a normally distributed population, it is more probable that a given data point will be drawn from the most densely concentrated area of the distribution, rather than one of the tails. As a data set becomes larger, the more the sample resembles the population from which it was drawn, increasing the likelihood of outlying values. There is only about a 1% chance you will get an outlying data point from a normally distributed population.

Before proceeding with any formal analysis researchers need to consider whether outlying data contains valuable information. If an outlier is a genuine result, it might indicate an extreme of behaviour that inspirers and requires further inquiry as to what makes these participants different and if can we learn from them.

Outliers can represent an error or genuine data, which must be examined carefully and should not be removed without justification.

Week 3: homework for my TA, comments I’ve made.
October 14, 2011, 7:46 pm
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