From the syllabus:

Content Focus

Modern scientific research involves making observations and measurements that produce ever-increasing amounts of data. All observations and measurements contain some uncertainty and error. Statistical methods help us quantify and characterise this uncertainty. Data usually contains patterns and trends available for analysis, using a variety of tools in order to derive meaning from them.

Students will develop knowledge, understanding and skills related to the analytical techniques for accessing, storing and analysing high-volume unstructured secondary-sourced publicly available data set(s). Students will be provided with the opportunity to consider limitations and construct plausible inferences while understanding degrees of uncertainty in the data. Students will explore the difference between the concepts of proving that something is true and measuring the probability of a certain result by understanding falsification and statistical levels of confidence.

Content

Patterns and Trends

Inquiry question: What tools are used to describe patterns and trends in data?

Students:

• analyse and determine the differences between data and evidence
• describe the difference between qualitative and quantitative data sets, and methods used for statistical analysis, including but not limited to:

– content and thematic analysis

– descriptive statistics

• select and use appropriate tools, technologies and/or models in order to manipulate and represent data appropriately for a data set, including but not limited to:

– spreadsheets

– graphical representations

– models (physical, computational and/or mathematical)

– digital technologies

• assess the relevance, accuracy and validity of the data and determine error, uncertainty and comment on its limitations
• evaluate the limitations of data analysis and interpretation

Statistics in Scientific Research

Inquiry question: How does statistical analysis assist in finding meaning in the trends or patterns in data sets?

Students:

• apply appropriate descriptive statistics to a data set(s), including but not limited to:

– mean

– median

– standard deviation

• apply appropriate performance measures to the statistical analysis of quantitative data set(s) obtained from conducting a relevant practical investigation, including but not limited to:

– error

– accuracy

– precision

– bias

– data cleansing

• apply appropriate performance measures to the statistical analysis of a data set(s) relevant to the Scientific Research Project
• apply appropriate statistical tests of confidence to a data set(s), including but not limited to:

– Student’s t-test

– Chi-squared test

– F-test

• apply statistical tests that can determine correlation between two variables, including but not limited to:

– correlation coefficient

• describe the difference between correlation and causation
• explain the requirements to establish causation
• use available software to apply statistical tests appropriate to a large data set(s) to assist with the analysis of the data

Decisions from Data and Evidence

Inquiry question: How is evidence used to make decisions in the scientific research process?

Students:

• assess the benefits of collective and individual decision-making
• analyse patterns and trends arising from the data set(s) related to the Scientific Research Project to:

– construct a relevant conclusion

– suggest possibilities for further investigation

• demonstrate the impact of new data on established scientific ideas, including but not limited to one of the following:

– gravitational waves on general relativity

– mechanisms of disease transmission and control

– prediction of natural disasters

– effects of chemical pollutants on climate

Data Modelling

Inquiry question: How can data modelling help to process, frame and use knowledge obtained from the analysis of data sets?

Students:

• evaluate data modelling techniques used in contemporary science associated with large data sets, including but not limited to:

– predictive

– statistical

– descriptive

– graphical