Statistical techniques
Recommended Prerequisites
Before taking this module it is advised that you should have passed:
or 2 of Level 8 modules in Bio/Env/Geog
GEO1PE
BIO1CB
ENV1GE
BIO2IP
GEO2EI
ENV2LE
or 2 of Level 8 modules in Bio/Env/Geog
Landscape Evolution (ENVU2LV)
People and the Environment (GEOU1PP)
Global Environmental Issues (GEOU2GE)
Introduction to Cell Biology (BIOU1CE)
Building Planet Earth (ENVU1BP)
Introduction to Physiology (BIOU2PH)
Prohibited Combinations
You may not take this module if you have previously passed:
Statistical Techniques (SCI4T4)
Module Description
Statistical techniques are fundamental for addressing quantitative questions and making inferences from data. Consequently, statistical tools are indispensable for addressing questions across the natural and social sciences.
In this module, you’ll learn the following important skills for working with environmental and biological datasets:
How to manipulate datasets and characterise their statistical properties.
Understanding and applying null hypothesis testing.
Applying the correct statistical test to data using statistical software.
Interpreting the results of statistical tests to make conclusions about scientific problems.
By developing an understanding of statistics, you will become better able to critically evaluate the scientific literature and conduct scientific research. Because literacy in statistics and a proficiency in using statistical software is critical for addressing all data-driven investigations, the skills that you learn in this module will be broadly relevant to solving a broad range of important problems. The UN has defined 17 Sustainable Development Goals (SDGs), which set out the world's roadmap to ending poverty, reducing inequality, and protecting the planet by 2030. Addressing any of these SDGs will rely on some data collection and analysis, meaning that the skills you learn in this module are potentially relevant for all of them.
Location/Method of Study
Stirling/On Campus, UK
Stirling
Module Objectives
This module is designed to familiarise you with:Statistical analysis and associated computing software to implement it.Hypothesis testing.Basic statistical techniques that are used in analysing data.Applications of statistical techniques to a range of environmental and biological data sets and problems.Describing and reporting statistical analysis in report writingAnswering unseen questions about statistical problems in a time-limited context
Additional Costs
There are no additional costs associated with this Module.
Core Learning Outcomes
On successful completion of the module, you should be able to:
manipulate datasets and characterise their statistical properties;
demonstrate an understanding of null hypothesis testing;
choose and apply the correct statistical test to unseen data using statistical software;
interpret the results of statistical tests in order to generate conclusive statements on scientific problems.
Introductory Reading and Preparatory Work
Lab workbook: http://bradduthie.github.io/SCIU4T4
Delivery
Directed Study 15 hours Large group presentation or talk on a particular topic
Directed Study 33 hours A session involving the development and practical application of a particular skill or technique
Directed Study 36 hours A meeting involving one-to-one or small group supervision, feedback or detailed discussion on a particular topic or project, online or in person
Directed Study 24 hours Assessment activity that takes place within a scheduled session, usually conducted under some form of examination or test conditions
Total Study Time 200 hours
Attendance Requirements
Your engagement with learning materials and activities and attendance at scheduled live sessions and other events is extremely important. Full engagement in your studies will enable you to get the most out of the course and help you perform at your best when it comes to assessment.
We expect you to engage with all aspects of this module and with your programme of study. You should:
· Engage with all module materials, activities, and online timetabled teaching sessions
· Actively participate in discussions and practical activities
· Prepare in advance of live sessions by undertaking the required reading and/or other forms of preparation
· Submit coursework/assessments by the due time and date
· Complete class tests and examinations at the specified time and date
· Make your module co-ordinator aware at the earliest opportunity if you experience problems which may impact on your engagement
· Inform the University of absence from study (planned or unplanned), e.g. illness, emergency as outlined at http://www.stir.ac.uk/registry/studentinformation/absence
· Respond to e-mails from your personal tutor, module co-ordinator or programme director and attend meetings if requested.
· Engage with in-sessional English language classes (if applicable)
We will monitor these aspects throughout each semester to check that you are fully participating and that you are coping well with your studies. Some activities may be prescribed, failure to engage with 2/3 of prescribed activities will result in your module grade being capped at the pass mark (40 for Undergraduate modules, 50 for Postgraduate modules).
Assessment
% of final
grade Learning
Outcomes
Class Test 0 1
Class Test 25 2
Class Test 25 2
Exam (Canvas - on campus) 50 1,2,3,4
Coursework: 50%
Examination: 50%
More information at: https://portal.stir.ac.uk/calendar/calendar.jsp?modCode=SCIU4T4&_gl=1*1l7ziqq*_ga*MTY1OTcwNzEyMS4xNjkyMDM2NjY3*_ga_ENJQ0W7S1M*MTY5MjAzNjY2Ny4xLjEuMTY5MjAzNzg0My4wLjAuMA..
Presential
TRUE
Statistical techniques
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HaDEA. Neither the European Union nor the granting authority can be held responsible for them. The statements made herein do not necessarily have the consent or agreement of the ASTRAIOS Consortium. These represent the opinion and findings of the author(s).
