1. Ruling out rival hypothesis
The principle propounds that the research outcome of any study ought to exhibit a certain high level of consistency with several other hypotheses. Sometimes consistency connotes that the research is well founded and has a firm basis. Moreover, it is essential for a research hypothesis to have similarity with other hypotheses so that there is a simple elimination of the inconsistent deductions of similar research projects. In the case of the research description, the study fails to show the consistency in its initial study with the research project that it undertakes later on. Hence, a research hypothesis styled “There is no correlation between the frequent usage of Twitter and its resultant negative effects on the attention span of an individual” can easily challenge the hypothesis posited in this research description. The reason for this is because the “TV experiment” and the “Twitter experiment” both aimed at coming to the same conclusion had differing results. The effect of this is that it is now difficult to rule out rivalling hypotheses because similar experiments show different results. Hence, it is safe to say that this research description has failed to meet this scientific principal as negative hypotheses can challenge its results.
2. Correlation v Causation
The principle of correlation and causation states that a link between two things statistically does not automatically create a cause and effect kind of relationship. It is imperative that critical analysis is done on data and hypotheses so that such assumptions are avoided. In some instances where two elements seemingly have a close link to one another, a third element can be the link between those two elements. Further, an interrogation should be made between two elements that are linked whether the causal relationship can be reversed and the net resultant effect to remain the same. In the case of the research description, the researchers fail to show the link between the element of Twitter usage and the resultant reduction in concentration span for the twitter users. The reason I am making this assertion is because by undertaking an experiment using a TV as a distraction and a tough puzzle as the element to be concentrated on, the researchers failed to prove how staring at the TV occasionally showed the adverse effects of twitter usage. There may be other variables that may be considered to have made the participants in the research stare at the TV from time to time. One of these factors would include gazing at the TV as one thinks of how to solve the puzzle instead of watching it. The complexities involved in determining the infinite number of issues that could have motivated the participants to stare at the TV are immeasurable, and the researchers erred in concluding that the evidence pointed at their hypothesis. Therefore, the first experiment conducted, even though showed a positive result, was not the actual pointer of determining whether the hypothesis was true or false.
3. Falsifiability
Falsifiability denotes the ability of any scientific hypothesis to be proved or disproved. The scientific principle is based on the fact that no scientific finding is full proof. Tests and experiments can be further conducted to an already existing scientific study to show that it was false or there are additional causes to a hypothesis that seemed only to have specific causes initially. The scientific principle of falsifiability is the reason for the advancement in technology and the face pace of changes in the field of technology and science since the advent of technology in the late 19th Century. In the case of the research description, it is possible to argue that the research findings of the study were false. First, the deduction would be premised on challenging the relationship between the first experiment and the hypothesis. One can easily argue that the mere fact that frequent Twitter users had the highest tendency of viewing the Television is questionable because other parameters can explain that apart from the hypothesis which is strictly stating that it is as a result of a short concentration span. Furthermore, the researcher Dr Twinge also demonstrates confirmation bias by applying the same standard that would apply to him to make deductions on the research findings. Hence, the research is reduced to an issue of confirming what he already knows as opposed to forming independent conclusions based on the experiment. Also, the fact that the second experiment was a failure can be another reason for questioning the integrity of the research findings. The uncertainty in the time that a Twitter user needs to have for them to have a limited concentration span is quite questionable. The experiment was unable to determine the period necessary for the hypothesis to be proved. Consequently, the research findings can be disproved on this ground too.
Warning signs related to pseudoscientific claims
Over-reliance on anecdotes
Lilienfeld gave the dangers of a single story as one of the tragedies in the scientific field (Lilienfeld et al., 2016). Science relies on data that has been collected over a relatively long period and done with a considerable number of people for meaningful conclusions to be drawn from it. One of the most significant errors in a scientific finding is to rely on one person’s experience as a representation of what would be experienced by many other people. In this research description, the researcher, Dr Twinge, gives her experience of joining twitter in 2007 and how it has, in turn, affected his concentration span over the years. There is a possibility that the hypothesis of the research description was drawn from his experience and hence he was biased during the whole research process as he was more concerned with proving his experience as opposed to having an objective, independent conclusions. The research can be heavily faulted for the confirmation bias by Dr Twinge. The fact that he relied on his experience to conduct the research is the reason why the process can be significantly faulted.
References
Lilienfeld, S.O., Lynn, S.J., Namy, L.L., Woolf, N.J., Cramer, K.M. " Schmaltz, R. (2016). Psychology: From inquiry to understanding. 3rd Canadian edition. Toronto, ON: Pearson.
