About Course
Programme purpose
Use modern statistical reasoning to make defensible decisions—not merely calculate a p-value.
Learning route
- Data, variation and measurement
- Sampling and survey design
- Descriptive statistics
- Probability and uncertainty
- Inference and hypothesis testing
- Regression and communication
Applied data exercises and evidence submissions are assessed through the Analytics Lab.
Practical Statistics for evidence-based decisions
This Practical Statistics course develops statistical understanding in the context of analysing and interpreting evidence. It is intended for learners who need statistics as an applied analytical capability rather than as an abstract subject alone.
Who this course is for
- Data and research professionals
- Monitoring and evaluation practitioners
- Students working with quantitative evidence
- Managers who interpret statistical information
- Analysts strengthening their statistical foundations
What you will explore
- Explore Statistical thinking and data design.
- Explore Measurement and data quality.
- Explore Describing data and variation.
- Explore Sampling and uncertainty.
- Explore Comparisons, relationships and inference.
- Explore Professional evidence checkpoint.
Course curriculum
The current Tutor LMS curriculum contains the following learning areas and activities.
Statistical thinking and data design
- Identify populations, samples, units and variables
- Match statistical questions to decision contexts
- Quiz: Practical Statistics — Theory Check
Measurement and data quality
- Use levels of measurement and appropriate summaries
- Detect bias, missingness and data-quality threats
Describing data and variation
- Interpret centre, spread, shape and outliers
- Choose useful tables and visualisations
Sampling and uncertainty
- Understand sampling error, confidence intervals and margins of error
- Interpret sample size and representativeness
Comparisons, relationships and inference
- Distinguish association from causation
- Interpret tests, effect sizes and model outputs responsibly
Professional evidence checkpoint
- Submit a statistical analysis plan, annotated output and evidence interpretation note in the Analytics Lab
Learning with DatalytIQs
DatalytIQs Academy connects structured learning with applied analysis, evidence-based thinking and professional practice. Use the course curriculum and learning activities as your primary pathway through this course.
Course Content
Statistical thinking and data design
-
Identify populations, samples, units and variables
-
Match statistical questions to decision contexts
-
Practical Statistics — Theory Check
Measurement and data quality
Describing data and variation
Sampling and uncertainty
Comparisons, relationships and inference
Professional evidence checkpoint
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