The evidence guide provides advice on assessment and must be read in conjunction with the performance criteria, required skills and knowledge, range statement and the Assessment Guidelines for the Training Package.

Overview of assessment

A person who demonstrates competency in this unit must be able to apply statistics and probability techniques to engineering-related problems within the context of specified engineering applications and other solution validation and technical oversight procedures. The candidate may demonstrate competence through either working individually or as part of a team.

Critical aspects for assessment and evidence required to demonstrate competency in this unit

Assessors must be satisfied that the candidate can competently and consistently:

solve engineering and manufacturing problems using statistics and probability techniques

validate results of simple examples using statistics and probability either analytically and/or graphically

assist decision making processes in industry by analysis of data using statistical and probability concepts and tools.

Context of and specific resources for assessment

This unit may be assessed on the job, off the job or a combination of both on and off the job. Where assessment occurs off the job then a simulated working environment must be used where the range of conditions reflects realistic workplace situations. The competencies covered by this unit would be demonstrated by an individual working alone or as part of a team.

Where applicable, reasonable adjustment must be made to work environments and training situations to accommodate ethnicity, age, gender, demographics and disability.

Access must be provided to appropriate learning and/or assessment support when required. Where applicable, physical resources should include equipment modified for people with disabilities.

Method of assessment

Assessment must satisfy the endorsed Assessment Guidelines of the MEM05 Metal and Engineering Training Package.

Assessment methods must confirm consistency and accuracy of performance (over time and in a range of workplace relevant contexts) together with application of underpinning knowledge.

Assessment methods must be by direct observation of tasks and include questioning on underpinning knowledge to ensure correct interpretation and application.

Assessment may be applied under project-related conditions (real or simulated) and require evidence of process.

Assessment must confirm a reasonable inference that competency is not only able to be satisfied under the particular circumstance, but is able to be transferred to other circumstances.

Assessment may be in conjunction with assessment of other units of competency where required.

Guidance information for assessment

Assessment processes and techniques must be culturally appropriate and appropriate to the language and literacy capacity of the candidate and the work being performed.

Required skills

Required skills include:

analysing engineering applications to determine relevant statistics and probability techniques

applying relevant statistical and probability concepts and tools and applying to engineering applications

using appropriate software to generate solutions to statistical and probability-related engineering problems

establishing appropriate procedures for checking and validating solutions

logical layout and presentation of data

reporting and effectively communicating the results of statistical and probability-based analysis

Required knowledge

Required knowledge includes:

meaning and appropriate application of discrete and continuous data

concepts of mean, median and mode and their application to engineering and manufacturing problems requiring statistics-based solutions

standard deviation, range, interquartile range and frequency distributions

use of appropriate software and/or scientific calculators to generate solutions to statistical and probability-related engineering problems and methods for presentation of data, such as:

frequency distribution tables

histograms

the concept of variance and its application to engineering and manufacturing situations

application of statistical modelling to engineering and manufacturing situations

translation of statistical data into engineering parameters and the applications to:

process improvement techniques

reliability analysis

concepts of probability:

single events

multiple events

with permutations and combinations

probability distributions:

normal

binomial

sampling distributions:

sample size

statistical significance

confidence intervals

application of probability-based solutions to engineering and manufacturing problems

The range statement relates to the unit of competency as a whole. It allows for different work environments and situations that may affect performance. Bold italicised wording, if used in the performance criteria, is detailed below. Essential operating conditions that may be present with training and assessment (depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts) may also be included.

Engineering applications related to statistics and probability techniques in this unit

Most engineering disciplines will have applications supported by the statistics and probability skills described in this unit, including mechanical, manufacturing, maintenance and mechatronics engineering. Examples of engineering applications requiring statistics and probability skills described in this unit may include:

assisting decision making in industry by analysis of data using statistical and probability concepts and tools

informing maintenance decisions and reliability analysis

identifying the most appropriate outcome of manufacturing trials

determining the effectiveness of process improvement techniques

Scope of statistical and probability techniques

The scope of statistics and probability techniques required for an engineering or manufacturing application will vary and may include:

use of discrete or continuous data

application of measures of central tendency, such as:

mean

median

mode

application of measures of distribution, such as:

standard deviation

range

interquartile range

analysis of statistical inference, such as:

sample size

statistical significance

application of probability concepts and tools, such as:

probability laws

probability distributions