The Conclusion from Sarah does not seem to be reasonable, and the conclusion is a conventional instance of identifying dissimilarity between an outcome and a statistically significant outcome. 10/5=0.67 which is 67%. Therefore, 67% of her colleagues decided to vote for Jones. Considering that this percentage is greater than 50% does not imply that Jones will win, as there is a need to put into consideration the divergence of your model outcomes (Zhou Zhou, 2020). Furthermore, consider that Sarah requested her colleagues to express their views. This builds a kind of response favoritism as Sarah might only have requested her female colleagues to express their views (and the possibility of them voting for a lady candidate is high if Jones is a lady). To be able to have a better insight of the population outlook based on which aspirant is more likely to emerge victoriously, we have to guarantee that Sarah examines her participants randomly, meaning that, Sarah should choose her participants randomly and then inquire from them who they would vote for. Picking people randomly ensures that we have a good perception of the whole population.
By adopting a normal distribution because we are applying a test of proportions, we might need around 30 people in the overall sample size to scrutinize robustness. A total of 30 Sara's colleagues will make our distribution appear normal, meaning that the outcome from our assessment will consecutively be more dependable and precise to assists us in comprehending the true population, or the precise population that will elect Jones.
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Sara's conclusion does not seem to be reasonable as she only considered distinctiveness and not the deviation in that distinctiveness (together with the distinctiveness itself) (Spanos, 2019)). We should also make sure that the overall number is represented which does not need one to choose his/her friends (people who are close to you), and above all, one needs to select individuals randomly.
References
Spanos, A. (2019). Probability Theory and Statistical Inference. Cambridge University Press.
Zhou Zhou, H. D. (2020). Statistical Inference for High Dimensional Panel Functional Time Series. Universitätsbibliothek Dortmund.
