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Evidence Guide: PUADEFTE006A - Analyse test data

Student: __________________________________________________

Signature: _________________________________________________

Tips for gathering evidence to demonstrate your skills

The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!

From the Wiki University

 

PUADEFTE006A - Analyse test data

What evidence can you provide to prove your understanding of each of the following citeria?

Perform scientific calculations

  1. Consistency of raw data with expectations and reasonable ranges is ensured
  2. Scientific quantities are calculated
  3. Calculated quantities and estimations are accurately determined.
  4. Results are presented using the appropriate units, uncertainties and number of significant figures
Consistency of raw data with expectations and reasonable ranges is ensured

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Scientific quantities are calculated

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Calculated quantities and estimations are accurately determined.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Results are presented using the appropriate units, uncertainties and number of significant figures

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Analyse trends and relationships in data

  1. Relationships are determined between sets of data
  2. Data are prepared and analysed to determine if a process is in control
  3. Possible causes for out-of-control condition are identified
  4. Organisational procedures are followed to return process to in-control operation
Relationships are determined between sets of data

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Data are prepared and analysed to determine if a process is in control

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Possible causes for out-of-control condition are identified

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Organisational procedures are followed to return process to in-control operation

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine variation and/or uncertainty in data distributions

  1. Raw data is organised into appropriate frequency distributions
  2. Statistical properties are calculated for ungrouped and grouped data
  3. Statistical properties are interpreted to determine characteristics of sample or population
  4. Standard deviations and confidence limits are calculated for means and replicates
  5. Uncertainty in measurements is determined using statistical analysis
  6. Data acceptability is determined using statistical tests and organisational procedures
Raw data is organised into appropriate frequency distributions

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Statistical properties are calculated for ungrouped and grouped data

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Statistical properties are interpreted to determine characteristics of sample or population

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Standard deviations and confidence limits are calculated for means and replicates

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Uncertainty in measurements is determined using statistical analysis

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Data acceptability is determined using statistical tests and organisational procedures

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Check for aberrant results

  1. Results that cannot be reconciled with documentation, testing procedures and/or expected outcomes are identified
  2. Appropriate actions are determined in consultation with supervisor as required
Results that cannot be reconciled with documentation, testing procedures and/or expected outcomes are identified

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Appropriate actions are determined in consultation with supervisor as required

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Maintain results

  1. Data is stored, retrieved and manipulated following document traceability procedures
  2. Results are presented in an appropriate format
  3. Entry of data and results are verified as correct
  4. Security and confidentiality of data is maintained in accordance with workplace and regulatory requirements
  5. Reports are prepared in a format and style consistent with their intended use and organisational guidelines
Data is stored, retrieved and manipulated following document traceability procedures

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Results are presented in an appropriate format

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Entry of data and results are verified as correct

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Security and confidentiality of data is maintained in accordance with workplace and regulatory requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Reports are prepared in a format and style consistent with their intended use and organisational guidelines

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Assessed

Teacher: ___________________________________ Date: _________

Signature: ________________________________________________

Comments:

 

 

 

 

 

 

 

 

Instructions to Assessors

Evidence Guide

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

Nil

Consistency in performance

Competency should be demonstrated over time and should be observed in a range of actual or simulated work contexts.

Context of and specific resources for assessment

Context of assessment

Competency should be assessed in the workplace or in a simulated workplace environment.

Specific resources for assessment

Access is required to:

computer and relevant software or laboratory information system

data sets and records

relevant workplace procedures.

Guidance information for assessment

In all cases, practical assessment should be supported by questions to assess required knowledge and those aspects of competency which are difficult to assess directly. Questioning techniques should suit the language and literacy levels of the candidate.

Assessment methods should reflect workplace demands such as literacy and the needs of particular groups.

Assessment methods suitable for valid and reliable assessment of this unit may include, but are not limited to, a combination of:

authenticated evidence from the workplace and/or training programs

case studies

demonstration

feedback from supervisors and peers regarding the candidate's ability

observation

portfolios

projects

questioning

reviews or reports prepared by the candidate

scenarios

simulation or role plays.

Required Skills and Knowledge

This describes the essential skills and knowledge and their level, required for this unit.

Required Skills

complete mathematical calculations such as fractions, decimals, ratios, proportions and percent

evaluate mathematical formulae

interpret scientific properties

prepare and interpret data

prepare and interpret graphs

Required Knowledge

characteristics of a valid measurement

procedures for data traceability

procedures for maintaining and filing records, security of data

procedures for verifying data and rectifying mistakes

relevant scientific terminology

sources of uncertainty in measurements

Range Statement

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 in the Performance Criteria is detailed below.

Data may include

Audio

Electronic (digital or analogue) recordings/logs

Imagery including satellite

Linear, semi-log and log-log graphs

Observations

Physical components

Physical evidence

Qualitative statements

Statistical results

Calculations of scientific quantities may include

Electrical properties: conductivity, resistivity, dielectric constants

Mathematical calculations such as: algebraic, logarithmic, exponential, power functions, means, medians, modes, ranges, standard deviations, regression line equations and correlation coefficients

Mechanical properties: stress, strain, elastic moduli, yield strength, hardness

Optical properties: absorbance/transmittance, path length, extinction coefficient, concentration (Beer's Law), detection limits

Percentage and absolute uncertainties in measurements and test results

Quantities associated with quality control monitoring, assessment and reporting

Thermal properties: heat capacity, thermal expansion, thermal conductivity, thermal resistance

Analysis may include

Graphical analysis including:

determination of linear, logarithmic, exponential and power relationships

regression lines and interpretation of correlation coefficients

Statistical analysis including:

analysis of variance (ANOVA)

data acceptability tests, such as Q, T and Youden

histograms, frequency plots, stem and leaf plots, boxplots, scatter plots

means, medians, modes, ranges and standard deviations

Pareto diagrams, Stewhart control charts, CuSum control charts

probability, normal probability plots

regression methods for calibration, linearity checks, comparing analytical methods

Organisations may include

Defence

Defence contractors and sub-contractors

Defence Materiel Organisation

Defence Science and Technology Organisation

International test agencies

Universities

Statistical properties may include

Frequency distributions

Means

Medians

Modes

Ranges

Standard deviations