Foundations of Statistical Inference

2000

There has been widespread concern expressed by members of the statistics education community in the past few years about the lack of any real understanding demonstrated by many students completing courses in introductory statistics. This deficiency in understanding has been particularly noted in the area of inferential statistics, where students, particularly those studying statistics as a service course, have been inclined to view statistical inference as a set of unrelated recipes. As such, these students have developed skills that have little practical application and are easily forgotten. This thesis is concerned with the development of understanding in statistical inference for beginning students of statistics at the post-secondary level. This involves consideration of the nature of understanding in introductory statistical inference, and how understanding can be measured in the context of statistical inference. In particular, the study has examined the role of the sampling dis...

2019

Statistics is a branch of mathematics, which deals with all aspects of data. In data analysis are primarily used two main statistical methods: descriptive statistics and inferential statistics. In our research the focus in on descriptive statistics. From descriptive statistics we are considering mean, mode, median, quartiles, variance and standard deviation, outlier, rang, boxplot, scatterplot, steam and leaf plot, mean deviation and mean absolute deviation. The methods listed above are presented through examples

Statistics in Criminal Justice, 2013

Mathematics Education Research Journal, 2003

Many statistics educators believe that few students develop the level of conceptual understanding essential for them to apply correctly the statistical techniques at their disposal and to interpret their outcomes appropriately. It is also commonly believed that the sampling distribution plays an important role in developing this understanding. This study clarifies the role of the sampling distribution in student understanding of statistical inference, and makes recommendations concerning the content and conduct of teaching and learning strategies in this area.

Educational studies in mathematics, 2002

We distinguish two conceptions of sample and sampling that emerged in the context of a teaching experiment conducted in a high school statistics class. In one conception 'sample as a quasi-proportional, small-scale version of the population' is the encompassing image. This conception entails images of repeating the sampling process and an image of variability among its outcomes that supports reasoning about distributions. In contrast, a sample may be viewed simply as 'a subset of a population'-an encompassing image devoid of repeated sampling, and of ideas of variability that extend to distribution. We argue that the former conception is a powerful one to target for instruction.

2000

Statistical inference is about using statistical data (x) to formulate an opinion about something that is dened well, but unknown ( y). Testing a hypothesis H about y is one of the possibilities, the estimation or prediction ofy is another one. We concentrate the attention on estimation or prediction in the sense that an opinion is required in the form