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Scientific statistical working in a team is a special challenge for students in high schools, especially with a civic statistical problem. Ideally, they are following the PPDAC cycle: they formulate a problem together, plan an investigation, collect the data, use software to analyze the data, and formulate results in a seminar paper. This article is written jointly by a student and a computer science teacher involved in the project, offering insights from both perspectives.

In this article I present an activity introducing statistical concepts to engineering students to help them develop inductive reasoning and problem-solving skills.

Research students in social science disciplines frequently struggle to master statistical analysis. A contributing factor may be the statistical software that is used, as the design of such software may not address the needs of non-statisticians or non-computer programming students. Hence, decisions about which statistical software tools are most suitable for such end-users need to be made at the introductory level. This paper first identifies key human-computer interaction (HCI) factors that may directly influence students' statistical analysis performance. Factors include technical properties such as user interface design, statistical features available, visualization, data handling, preparation, and manipulation, and usage properties such as speed/number of steps, ease of command/use, and efficiency. Four popular software systems (ie, SPSS, R within RStudio Desktop, R Commander & jamovi) were evaluated. Findings suggest that HCI usage factors from an interaction perspective are likely to be especially important for students gaining an introductory knowledge of statistics.

Undergraduate students tend to struggle with probability in their introductory statistics course. Probability problem solving requires several steps. First, students must make sense of the probability scenario, then determine the appropriate probability rules, and finally, execute the procedures to solve the problem. With no previous exposure to probability, this presents too great a cognitive load for many students. Using worked-out problems then transitioning to partially worked-out problems in an introductory statistics course at a large university helped students succeed at solving probability problems. The worked-out problems included writing prompts to encourage self-explanation of students' thinking through studying the worked-out examples. This paper explains the use of these instructional principles and their implementation in an introductory statistics course for non-STEM majors, resulting in higher student achievement and understanding.

Posing statistical questions is a fundamental and often overlooked component of statistical inquiry. In this paper, we provide an overview of shared understandings regarding what constitutes a good statistical question. We then describe three approaches--a checklist for improving statistical questions, a three-phase feedback activity, and a matching game--that we find useful with preservice teachers to support the development of high-quality statistical questions for the comparison of data sets.

The aim of this paper is to approach the teaching of the Poisson distribution in a friendly and amusing way. It constitutes a common practice to adopt the main probability distributions in order to predict results of sport events and to estimate the win return of betting activities (Chalikias 2009). In particular, by using the Poisson distribution, we can calculate the probability of the final score in a football match, and then we can compare this probability with the values that various betting companies offer. Combining this information with the average of goals for both, it is possible to make a decision as to which betting company is more profitable for the betting players. The case study of the English Premier League is examined.

This paper presents insights from classroom practice where mathematical thinking, and in particular statistical literacy, are central to solving a STEM problem. We demonstrate that when an investigation sparks student interest and requires active participation, there are opportunities to experience the functionality of statistics.

College-level statistics courses emphasize the use of the coefficient of determination, R-squared, in evaluating a linear regression model: higher R-squared is better. This often gives students an impression that higher R-squared implies better predictability since textbooks tend to use sample data to support the theory and students rarely have an opportunity to work on real data. In this paper, health care stocks are used as predictors and the result demonstrates that high R-squared does not necessarily mean high predictability and that multiple linear regression can be used in the study of data behavior. In particular, by learning the pattern of the near and far out-of-sample-prediction errors for different time periods throughout a dataset, the near out-of-sample prediction errors can be used to control the prediction errors and identify a subset of predictors that can well reflect the trend of S&P 500.

In this paper, we investigate an interesting question that came up when reading a problem in a school textbook: What happens to the variance of a dataset in the case of changing one single data point, and why? Some of the answers are not surprising but here we find the full answer and demonstrate the understanding of it suitable for school students.

There is much debate about the place of probability in an introductory statistics course. While students may or may not use probability distributions in their post-collegiate lives, they will likely be faced with day-to-day decisions that require a probabilistic assessment of risk and reward. This paper describes an innovative way to teach students in an introductory statistics course how to reason about probability and expected value through the board game Camel Up. The in-class activities described here can be scaled to fit the needs and abilities of the students in a wide range of introductory statistics courses at a variety of levels.

The use of real contexts and history in the teaching of statistical principles holds much attraction in the classroom. This is especially so when the examples used are in situations that are quite out of the ordinary but can lead to further investigation by the students. Previous instances of these have been used with great effect with sporting data and even courtroom decisions. In this article, a very practical problem in forensic science is tackled using elementary statistics with some spectacular and extremely useful results. The use of real contexts and history in the teaching of statistical principles holds much attraction in the classroom. This is especially so when the examples represent unusual situations but can lead to further investigation by the students. Previous instances of this have been utilized to great effect with sporting data and even courtroom decisions. This paper tackles a very practical problem in forensic science using simple linear regression, with some spectacular and extremely useful results.

This paper describes a lesson centered around a metric for measuring partisan gerrymandering: the efficiency gap. We situate the efficiency gap in the current US political climate, describe the activity itself, and then provide recommendations for readers interested in adapting the lesson for their own classrooms.

A formula is derived for a 'two-dice horse race', in which two ordinary dice are thrown repeatedly and each time the sum of the scores determines which horse (numbered 2 to 12) moves forward one space. This paper answers a question posed in a former "Teaching Statistics" article, and demonstrates the value of simulation.

Because of the increasing use of business analytics to improve business processes, it is not difficult to argue the importance that undergraduate business students gain the ability to understand, interpret, and communicate statistics. However, a task that may be difficult for statistics instructors is to quickly demonstrate and convince business students the importance of statistics. This paper presents an assignment that utilizes the Occupational Information Network (O*NET) to demonstrate the importance of understanding basic statistical concepts such as population parameters, sampling methods, descriptive measures, and displaying data. This can be administered to an introductory business statistics course in order to drum up students' initial interest and views of applicability of statistics.

Class examples for a standard introductory statistics course usually involve a variety of applications. In this paper, we consider the teaching of statistics using forensic science and the law, an area that holds some fascination with many audiences.

This paper describes an activity to celebrate the lives of Florence Nightingale and Florence Nightingale David in statistics classrooms.

This paper presents a sample assignment to be administered after the first two weeks of an introductory business focused statistics course in order to promote student buy-in. This assignment integrates graphical displays of data, descriptive statistics and cross-tabulation analysis through the lens of a marketing analysis study. A marketing sample was chosen because marketing is a tangible business practice to which all students, regardless of their background, have been exposed. The purpose of the assignment is to quickly cement in the students minds both the applicability of the course material within the business world as well as their own ability to master it.

In this paper, the dice game "Unders and Overs" is described and presented as an active learning exercise to introduce basic probability concepts. The implementation of the exercise is outlined and the resulting presentation of various probability concepts are described.

This paper deals with the analysis of cricket match results from the ICC World Cup 2011. We believe that such data provide good material for interesting classroom exercises. (Contains 7 tables and 1 figure.)

This classroom activity is designed to help students practice calculating percentiles. The approach of the activity involves physical sorting and full classroom participation in each calculation. The design encourages a more engaged approach than simply having students make a calculation with numbers on a paper.

This paper describes a case study for introductory statistics courses that promotes critical thinking in relation to causation.