Statistical inference is the theory, practice of methods of making judgments about a population and how reliable statistical relationships can be based on the random sampling of data ( Zacks, 2014 ) . In other terms, it can be defined as the process of formulating specific of a particular probability distribution through analyzing of data. In statistical reference for n individual to conclude a study there has to be an assumption of a population that I typically more significant than the data set.
Sara's conclusion was not logical for it lacked the criteria that are supposed to be adhered to when sampling data. To some extent, it was a bias conclusion because only a few individuals were represented in the process of her judgment. When doing such research, she was required to sample data from all the ethnic groups from all the two sides of the political divide and sample the views in groups ( Zacks, 2014 ). Some of the requirements for a viable and independent statistical analysis are that the sample should be random and should be less than one-tenth of the total population. Sara's statistical analysis was not random and did not put the population factor into consideration.
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Sara should sample one-tenth or a number that is less than of friends from the total population that is viable to vote. The reason for picking the ten percent is to make the selection more independent. For instance, when you settle for numbers above ten percent then the probability of selecting the same observation twice is high. Sara should focus on making a random selection of friends from all the sections of the country and should focus on the margin of the ten percent of the population. The reason why she should focus on these two main issues is not to arrive at accurate results or conclusions but to make them more independent and reliable.
References
Zacks, S. (2014). Parametric Statistical Inference: Basic Theory and Modern Approaches (Vol. 4). Elsevier.
