ECON625 Data, Deception, and Decisions
The Complete Study & Assessment Guide · S2 2026ECON625 Overview
- Faculty of Business, Economics and Law
- Semester 2, 2026
- a Level 6 undergraduate course
- 15 points
- a business data-literacy and evidence-based decision course
ECON625 covers reliable data collection, descriptive statistics and visualisation, probability and risk, relationships, experiments and observational data, regression, control of external factors and ethical data use. It is taught within Faculty of Business, Economics and Law. It is a Level 6 undergraduate course. It carries 15 points.
- Data are made Every dataset reflects collection, inclusion and measurement choices.
- A chart is an argument Scale, denominator and grouping determine what a visual invites the reader to conclude.
- Correlation is not intervention Association can arise through reverse direction, confounding or selection.
- No exam Assessment culminates in a 50% individual data collection and presentation.
What you get in the full ECON625 Study & Assessment Guide
- 8 chapters, 23 pages — every assessed topic, week by week
- 11 original practice questions, each with a model answer
- 8 worked examples, solved the way ECON625 marks
- 19 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Data Collection and Presentation (50%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How ECON625 is assessed
| Component | Weight | Format |
|---|---|---|
| In-Class Case Study | 20% | Group exercise in Week 6 |
| In-Class Empirical Demonstration | 30% | Individual computer-based exercise in Week 9 |
| Data Collection and Presentation | 50% | Individual take-home exercise due by Week 12 |
Current assessment is a 20% in-class group case study in Week 6, a 30% individual in-class empirical demonstration in Week 9, and a 50% individual data collection and presentation due by Week 12. The official descriptor's overall pass-requirements field is blank and no extra component hurdle is published.
What other students are asking about ECON625
- Where can I find lecture notes or study guides for ECON625 at AUT?
- The ECON625 study guide in AskSia Library covers Data, Deception, and Decisions in 8 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in ECON625?
- No final exam is listed in the offering this AskSia Library guide is built from; ECON625 is assessed by In-Class Case Study 20%, In-Class Empirical Demonstration 30%, Data Collection and Presentation 50%.
- What does ECON625 cover on descriptive statistics and effective visualisation?
- In AskSia Library's ECON625 guide, the Descriptive Statistics and Effective Visualisation chapter explains median · standard deviation · visual encoding · choose numerical and visual summaries suited to scale and decision.
- What does ECON625 cover on probability, risk and decision-making?
- In AskSia Library's ECON625 guide, the Probability, Risk and Decision-Making chapter explains probability · expected value · risk · compare uncertain alternatives with outcomes and risk tolerance visible.
- What does ECON625 cover on correlation, association and statistical deception?
- In AskSia Library's ECON625 guide, the Correlation, Association and Statistical Deception chapter explains correlation · outlier · spurious association · calculate and interpret correlation with plots and data checks.
Current ECON625 dates
| Date | Item | Control |
|---|---|---|
| Week 6 | In-Class Case Study | 20% group exercise. |
| Week 9 | Empirical Demonstration | 30% individual computer-based exercise. |
| Week 12 | Data Collection and Presentation | 50% individual take-home exercise. |
Dates are as published in the current course materials. Confirm exact deadlines and submission settings in the live LMS.
What ECON625 covers
The sequence opens at Reliable Data, Populations and Measurement, develops its central analytical shift in Experiments, Observational Data and Causality, and closes with Ethical and Responsible Data Communication.
Reliable Data, Populations and Measurement
population · sample · measurement validity · evaluate whether observations support a stated business or policy populationDefine population
The course material gives this chapter a concrete anchor: The descriptor opens with understanding and collecting reliable data, and Week 1 slides address research and evidence.
- population
- sample
- measurement validity
- Applying population
- Limits of sample and measurement validity
Descriptive Statistics and Effective Visualisation
median · standard deviation · visual encoding · choose numerical and visual summaries suited to scale and decisionDefine median
The course material gives this chapter a concrete anchor: Current lecture slides directly cover descriptive statistics, mean, median, spread and visualisation.
- median
- standard deviation
- visual encoding
- Applying median
- Limits of standard deviation and visual encoding
Probability, Risk and Decision-Making
probability · expected value · risk · compare uncertain alternatives with outcomes and risk tolerance visibleDefine probability
The course material gives this chapter a concrete anchor: Probability, risk and decision-making are an explicit descriptor content area and Workshop 1 provides applied questions.
- probability
- expected value
- risk
- Applying probability
- Limits of expected value and risk
Correlation, Association and Statistical Deception
correlation · outlier · spurious association · calculate and interpret correlation with plots and data checksDefine correlation
The course material gives this chapter a concrete anchor: Current Week 2 lecture notes directly define correlation and its properties and separate it from causality.
- correlation
- outlier
- spurious association
- Applying correlation
- Limits of outlier and spurious association
Experiments, Observational Data and Causality
random assignment · observational study · confounder · distinguish association from credible intervention evidenceDefine random assignment
The course material gives this chapter a concrete anchor: The descriptor explicitly contrasts experimental and observational data; current notes develop direct, indirect and confounded causal stories.
- random assignment
- observational study
- confounder
- Applying random assignment
- Limits of observational study and confounder
Regression for Business and Policy Decisions
regression coefficient · residual · confidence interval · fit and interpret a regression with residual and uncertainty checksDefine regression coefficient
The course material gives this chapter a concrete anchor: Regression analysis for business strategy and policy impact is explicit course content and current notes lead from scatterplots to fitted lines.
- regression coefficient
- residual
- confidence interval
- Applying regression coefficient
- Limits of residual and confidence interval
Controlling External Factors and Robustness
multiple regression · omitted-variable bias · robustness check · control relevant external factors and compare specificationsDefine multiple regression
The course material gives this chapter a concrete anchor: Advanced techniques for controlling external factors are explicit official course content.
- multiple regression
- omitted-variable bias
- robustness check
- Applying multiple regression
- Limits of omitted-variable bias and robustness check
Ethical and Responsible Data Communication
data ethics · uncertainty communication · reproducibility · make a statistical claim auditable for source, uncertainty and harmDefine data ethics
The course material gives this chapter a concrete anchor: Ethical data use closes the official sequence and final assessment.
- data ethics
- uncertainty communication
- reproducibility
- Applying data ethics
- Limits of uncertainty communication and reproducibility
It is positioned as a business data-literacy and evidence-based decision course.
The course makes misleading statistics and decision communication central, ending with a 50% individual data collection and presentation rather than an exam.
Assessment in econ625 is distributed as follows: a 20% Week 6 group case study, 30% Week 9 individual empirical demonstration and 50% Week 12 individual data collection and presentation
The operational assessment conditions matter here.
No final examination is published; the final major task is the 50% individual data collection and presentation.
What makes econ625 demanding is concrete: distinguishing a numerically correct summary from a decision-ready claim by auditing how data were produced, what comparison is credible and which uncertainty or confounder survives
The official current descriptor publishes no extra overall or component pass requirement; the normal AUT course pass standard applies.
For enrolment planning, None; the official descriptor lists ECON622 as a restriction.
The sequence opens at Reliable Data, Populations and Measurement, develops its central analytical shift in Experiments, Observational Data and Causality, and closes with Ethical and Responsible Data Communication.
Worked example · free
Repair a misleading average
- 1Inspect distribution and outlier legitimacy.
- 1Calculate median and selected percentiles.
- 1Show a distribution plot with honest scale.
- 1Explain whether the decision concerns total payroll or typical employee.
- 1Report both mean and robust summaries with denominator.
Key terms
- population
- Complete set of units about which a study intends to learn. This chapter uses the concept when students evaluate whether observations support a stated business or policy population.
- sample
- Observed subset of units used to estimate or explore population features. It helps explain the reasoning required to evaluate whether observations support a stated business or policy population.
- measurement validity
- Degree to which an operational variable represents the intended construct. Its limit matters because large samples do not repair selection, nonresponse or invalid measurement.
- median
- Middle ordered value or midpoint of the two middle values in a sample. This chapter uses the concept when students choose numerical and visual summaries suited to scale and decision.
- standard deviation
- Square root of average squared dispersion under a stated sample or population convention. It helps explain the reasoning required to choose numerical and visual summaries suited to scale and decision.
- visual encoding
- Mapping from data values to position, length, colour, area or other graphical properties. Its limit matters because an average without spread, denominator and distribution can conceal heterogeneity.
- probability
- Numerical representation of uncertainty under a defined model and event space. This chapter uses the concept when students compare uncertain alternatives with outcomes and risk tolerance visible.
- expected value
- Probability-weighted average outcome over possible states. It helps explain the reasoning required to compare uncertain alternatives with outcomes and risk tolerance visible.
- risk
- Uncertainty relevant to objectives, including likelihood, consequence and distribution. Its limit matters because expected value alone can hide tail loss, dependence and asymmetric preferences.
- correlation
- Standardised linear co-movement between two variables. This chapter uses the concept when students calculate and interpret correlation with plots and data checks.
- outlier
- Observation unusually distant or influential relative to the rest of a dataset. It helps explain the reasoning required to calculate and interpret correlation with plots and data checks.
ECON625 FAQ
Where do students usually lose marks in econ625?
distinguishing a numerically correct summary from a decision-ready claim by auditing how data were produced, what comparison is credible and which uncertainty or confounder survives
How is econ625 assessed?
a 20% Week 6 group case study, 30% Week 9 individual empirical demonstration and 50% Week 12 individual data collection and presentation
What is the econ625 final assessed-task format?
No final examination is published; the final major task is the 50% individual data collection and presentation.
Does econ625 have a hurdle or component-level pass rule?
The official current descriptor publishes no extra overall or component pass requirement; the normal AUT course pass standard applies.
Which offering does this econ625 guide cover?
It is aligned to Semester 2, 2026; confirm your enrolled class and timetable in the current institutional system.
What prerequisites or restrictions apply to econ625?
None; the official descriptor lists ECON622 as a restriction.
Questions students ask in each ECON625 chapter
- What is the main task in Reliable Data, Populations and Measurement?
- Evaluate whether observations support a stated business or policy population. Chapter 01 · Reliable Data, Populations and Measurement
- What is the main task in Descriptive Statistics and Effective Visualisation?
- Choose numerical and visual summaries suited to scale and decision. Chapter 02 · Descriptive Statistics and Effective Visualisation
- What is the main task in Probability, Risk and Decision-Making?
- Compare uncertain alternatives with outcomes and risk tolerance visible. Chapter 03 · Probability, Risk and Decision-Making
- What is the main task in Correlation, Association and Statistical Deception?
- Calculate and interpret correlation with plots and data checks. Chapter 04 · Correlation, Association and Statistical Deception
- What is the main task in Experiments, Observational Data and Causality?
- Distinguish association from credible intervention evidence. Chapter 05 · Experiments, Observational Data and Causality
- What is the main task in Regression for Business and Policy Decisions?
- Fit and interpret a regression with residual and uncertainty checks. Chapter 06 · Regression for Business and Policy Decisions
- What is the main task in Controlling External Factors and Robustness?
- Control relevant external factors and compare specifications. Chapter 07 · Controlling External Factors and Robustness
- What is the main task in Ethical and Responsible Data Communication?
- Make a statistical claim auditable for source, uncertainty and harm. Chapter 08 · Ethical and Responsible Data Communication
How to prepare for the assessments
Retrieve the course map, practise the recurring method—define the population, unit, variables, data-generating process and decision, clean and visualise transparently, calculate a suitable statistic or model, then challenge selection, causality, uncertainty, ethics and communication before recommending action—on changed scenarios, and verify every operational assessment detail in the live institutional system.
Your AI Data Literacy tutor for ECON625
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