The Challenge of Developing Statistical Literacy, Reasoning and Thinking

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They help form the basis of our democracy and provide us with essential knowledge to assess the health and progress of our society. We rely on those statistics being visible, accessible and robust, and we rely on statistically literate people making best use of the information to determine our future action, by presenting clear and convincing arguments and developing 'evidence-based policy' to guide our decision making.

We are surrounded by facts and figures everyday. An incredible one in two households in Australia had a Cat Stevens album in the seventies. They are indicators of change and allow meaningful comparisons to be made. In order to make sound judgements, it is essential that we are equipped with the very best knowledge for research, planning and decision-making purposes. While it may be the issues rather than the statistics that grab people's attention, it should be recognised that it is the statistics that inform the issues.

Statistical literacy, then, is the ability to accurately understand, interpret and evaluate the data that inform these issues.

The Influence of Statistical Anxiety on Statistic Reasoning of Pre-service Mathematics Teachers

Back to top Why is it important to be statistically literate? The provision of accurate and authoritative statistical information strengthens our society. It provides a basis for decisions to be made on public policy, such as determining electoral boundaries and where to locate schools and hospitals. It also allows businesses to know their market, grow their business, and improve their marketing strategies by targeting their activities appropriately.

In today's information-rich society, being statistically literate will give you an edge. It will make you more attractive to future employers and put you ahead of your competitors in the workplace. Broadening your statistical knowledge will enable you to engage in discussions and decision-making processes with authority, accuracy and integrity. Back to top Are you statistically literate?

The Congress

If you are uncomfortable with using statistics, you are not alone. Many people shy away from using statistics because of their perceived complexity. People may: not know where to look to find the information they need; be unfamiliar with the terminology; or lack confidence in their ability to make sense of the numbers. You do not have to be an expert at maths to work with statistics. Numeracy implies a basic competence in mathematics, a basic understanding of numbers and figures. It is certainly a prerequisite to being statistically literate, but statistical literacy is not about being adept at formulating or understanding the methodology behind the numbers.

Rather, it is the ability to interpret the numbers and communicate the information contained therein effectively. Statistics simply help to tell a story. They may be presented in different ways, such as tables, graphs, maps or text, but they are not scary or boring if you know what they mean.

Statistics Is For Everyone

Increased use of statistics does not automatically lead to an increased understanding of statistics. In this information-rich age, it is important for individuals to be independent, critical thinkers, and statistical literacy is fundamental to achieving this. Be sceptical. Consider what spin may have been put on the data. What has really been said and what has been left out? Be aware. Ignoring definitions or comparing statistics inappropriately can result in misinterpretation of the data. Back to top Statistical literacy criteria To be statistically literate, there are four critical areas in which you need to build skills: 1.

Data awareness Are the data relevant and appropriate? Data are the basis of statistics. Data are observations, which when organised and evaluated become information or knowledge. The amount of data available can be overwhelming. Interpreting data accurately requires a systematic approach. Think about the questions you need the data to answer.

An important aspect of statistical literacy is understanding what makes data trustworthy and reliable.

The Challenge of Developing Statistical Reasoning

Understanding how data are produced ensures that informed judgements can be made about the quality of the data. Where did the data come from? Data can come from a variety of sources. Beware of: Pre-existing data These may have been produced for a specific purpose. The population that the data are based on may differ from the population now under scrutiny, or the sampling method may not necessarily be appropriate for the current study.

Secondary data These may have been used in a selective way to suit the purpose of a particular study or report. As such, it may not be a reliable data source or be presenting the data in a manner consistent with the intent of the original data. As a general rule, consult the original or primary data source wherever possible. Data can be collected from a population as a whole or from a sample, from which conclusions can be drawn about the broader population. Types of sampling can vary, but the main thing to keep in mind is that any sample should be representative of the population.

If there are limitations with the sampling procedure, it is important that these limitations are acknowledged because they can influence the validity and reliability of the results. Example In a street poll the people used in a sample are generally chosen because they are readily available and willing to participate.

As a result, bias may be introduced because the sample is not truly representative of the population and the survey findings may be misleading. Anecdotal evidence This often relates to a specific event and is generally not representative.

go to link While it may be useful when describing a particular case study, care should be taken when making conclusions about the broader population. Biased data Bias can be deliberately or inadvertently introduced into survey samples. Sources of bias include: sample bias was the size of the sample appropriate and how were the respondents selected?

How were the data collected? There are three main forms of data collection: Self-enumeration People fill in their own forms and can complete them in their own time. This collection method may place limitations on the number and complexity of questions that can be asked, while responses may lack detail or accuracy. The Census is an example of self-enumeration. Interview based surveys An interviewer contacts the selected survey participant either in person or via telephone.

This collection method generally results in higher response rates, but also introduces the risk of interviewer bias. More questions and more complex questions can be asked, with more accurate and more detailed responses usually given. Administrative by-product Data are available through administrative records generated from the administrative transactions carried out by government departments, agencies and businesses, such as birth and death statistics, and overseas arrivals and departures. Making use of this type of data helps to keep the number of surveys and censuses to a minimum, which in turn is more cost effective.

The study was conducted in one class of 33 people of 3rd semester students who attended the basic statistics course.

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There were 10 men and 23 women. The sample selection was done considering the material taught in basic statistics courses and emphasized more on the matters which may be used by the students as the provision for teaching either at junior high school or senior high school. The data collection technique done in this research consisted of two stages: acquiring quantitative data with the statistical reasoning test and statistical anxiety questionnaire, after that, the observation and interview were done to explore the result in the quantitative stage.

The instrument used to collect data is a matter of statistical reasoning test adopted from the instrument developed by Chan et al. The anxiety factors found in Earp's study were sorted into three conditions, namely when taking courses, while studying learning and training and when examining. The results of statistical reasoning and anxiety tests were analyzed using statistical regression tests to determine the relationship between statistical anxiety and statistical reasoning.

These results were explored with the help of interviews and observations. Interviews were only conducted on 6 students with criteria determined by the researchers based on the results of statistical reasoning tests and statistical anxiety questionnaire. Observations were done during basic statistical learning and during statistical reasoning tests. The statistical anxiety analysis result, which wasapplied to 33 students, is presented in the following table.

Table 1 showed that overall statistical anxiety of pre-service mathematics teacher is at a moderate level in both men and women. If we look carefully at table 1 , it shows that the anxiety characteristics in both men and women are similar. It is also reinforced by interviews and observations during learning, where students feel comfortable during statistical learning. They can understand and do the exercises with the guidance of courses. When taking a course, students do not feel anxious because they are ready with their chosen course of study.

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It's just that they feel very anxious when the date of the exam is announced. Sometimes, before the exam, the tension will be very visible in between the students, with them usually asking about the learning materials, especially on result interpretation. Therefore, anxiety at an exam activity is at a high level. The reason for their anxiety is because they are afraid of not being able to answer the questions and get a low score. If you look at the observation results, here is where students feel tense when they are about to face exams and have to prepare better for the exam, this means statistical anxiety becomes the motivation for students to learn.

This is consistent with Thorndike's view of stimulus-response, i. In this case, anxiety is manifested as a reaction to learning cues that statistics as a subject is one of the hardest courses and only smart people can do well. Students with higher level intelligence criteria are even more prepared for the exam.