MICRO-CREDENTIAL IN UNDERSTANDING BIOLOGICAL DATA: EXPERIMENTAL DESIGN AND STATISTICAL ANALYSIS USING ANOVA

About this Module

What you will learn

The Understanding Biological Data: Experimental Design and Statistical Analysis microcredential program offers students hands-on training in essential statistical meth analyzing their final-year research data. This program focuses on three key analyses: O ANOVA, Two Way ANOVA, and Mixed Design ANOVA. Students will use SPSS and XLSTAT software to analyze their data, ensuring their findings are scientifically rigorous and re Through in-person classes, students will gain the skills to design experiments, apply the statistical tests, and interpret results confidently. By combining theory with practical experience, this program empowers students to tackle complex biological research and produce high-quality analyses for their final-year projects. Whether you're studying biology, environmental science, or other life sciences, this microcredential will help yo master the statistical tools essential for your research.

What skills you will gain

Experimental Design, Biological Data Analysis, Statistical Reasoning, One-Way ANOVA, Two-Way ANOVA, Mixed Design ANOVA, XLSTAT Data Analysis, Interpretation of Statistical Results, Scientific Reporting, Critical Thinking, Problem-Solving

Total contents and assessments

original teaching videos, 3 learning activities, 3 practical data analysis exercises using XLSTAT,

Module Details

CLUSTER : Science & Technology ( ST )
MODE/DURATION : Flexible
LENGTH : 2 days
EFFORT : 5
LEVEL : Intermediate
LANGUAGE : English
CERTIFICATE : Yes
CPD POINT : 0
PRICE : Free

Associated Course (s) :
No Course

 Syllabus

Purpose of ANOVA
Variation within and between groups
Factors, levels, response variables
Null and alternative hypotheses
F-value and p-value.

One-factor experimental designs
Biological and ecological examples
Assumptions and hypotheses.

Preparing data
Running One-Way ANOVA in XLSTAT
Interpreting ANOVA tables
Post-hoc comparisons
Reporting findings

Two-factor experimental designs
Main effects; interaction effects
Examples from morphometrics, population studies, growth, feeding behaviour and locomotor behaviour.

Organising factorial data
Performing Two-Way ANOVA in XLSTAT
Interpreting Factor A, Factor B and interaction effects

Between-subject factors
Repeated or within-subject factors
Repeated observations of the same experimental units
Biological examples.

Preparing repeated-measures datasets
Conducting Mixed Design ANOVA
Interpreting between-group effects
Repeated-factor effects and interactions; reporting findings.

Our Instructor

DR. TUN MOHD FIRDAUS BIN AZIS

Course Instructor
UiTM Kampus Arau
4.3 (average sufo) instructor rating 12 course(s)