Top Tips for teaching EVALUATION SKILLS in science
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Below you will find the next in our series of Top Tips designed to help you meet some of the challenges faced when teaching science skills under the new national curriculum.  For more information please visit or why not look at our Science Blog.

Top Tip

Evaluation – Comparing Predictions with Data and critically evaluating scientific data and the method used to generate it.

One of the most difficult skills for students of science to master is to be able to assess whether their experiment / investigation generated data that is of any value. i.e. Is the data they generated from the numbers of insects counted in an area - to the colours identified in a chromatogram - are valid, reliable and without any anomalies?

When Evaluating experimental data, the following key steps need to be undertaken:
  • State clearly if the data matches or does not match a prediction and where.
  • State clearly if there were any anomalies and where they occur.
  • State clearly how they could prevent the anomalies reoccurring next time.
  • If they repeated the experiment to make the data more reliable, accurate, precise and valid, how would they do it.
If pupils can do all of these – they will be well on the road to being able to evaluate an investigation!

An Investigation used as an example for developing the Skill of Evaluating.
  • Context:  An investigation is carried out, using blocks of different materials to examine which material conducts heat most effectively. Try to get at least metal, wood, glass (an upturned tumbler!) and plastic.  A pupil predicted that a plastic block would cause an ice cube to melt faster because it felt warmer than the rest.
  • ‘Pupils’ Method:
    1. Obtain some ice (similar size cubes)
    2. Predict where the ice melts fastest, slowest
    3. Place ice on blocks of different materials
    4. Record how long before the ice has melted.
  • Identifying the Pattern (Pupils Results Table):

  • Evaluating the Data - Can ‘you’:
  1. Say whether the data supports the pupil’s prediction?  If not, why not?
  2. Say if there seem to be any anomalies?  If so, where might they be?
  3. Suggest 5 things the student needed to do in their ‘method’ to ensure that their data was ‘Valid’?
  4. Suggest why the data is not ‘reliable’?
  • National Curriculum Assessment Match:
 Skills: recording findings using simple scientific language, drawings, labelled diagrams, keys, bar charts, and tables, reporting on findings from enquiries, including oral and written explanations, displays or presentations of results and conclusions, using results to draw simple conclusions, make predictions for new values, suggest improvements and raise further questions.
  1. Knowledge: Changes of state, solids, liquids and gases.
Remember: NEVER use the term FAIR TEST – it is meaningless! Instead, insist that pupils explain why their data is Valid and Reliable and provide evidence of why this is the case.

Free Science Training for Teachers at Your School

We off schools after-school teacher training, whereby our qualified science teachers come to your school and put your staff through a series of hands-on, practical science demonstrations and investigations.  We show what the new national curriculum requires, what the challenges are, and how to meet them!  For more information and booking please visit our "Twilight" zone.

From the National Curriculum 

The national curriculum for science aims to ensure that all pupils:
  • develop scientific knowledge and conceptual understanding through the specific disciplines of biology, chemistry and physics
  • develop understanding of the nature, processes and methods of science through different types of science enquiries that help them to answer scientific questions about the world around them
  • are equipped with the scientific knowledge required to understand the uses and implications of science, today and for the future.


Forming a Question
Identifying Variables
Making Predictions
Justifying predictions
Writing a method
Equipment lists
Risk assessments


Identifying what data to collect
How to present data
Designing a results table
Identifying patterns in Data
Drawing Graphs
Identifying ‘dodgy’ data


Checking if results match predictions
Explaining how results match / don’t match predictions
Identifying Anomalies and explaining them
Explaining how to ensure reliability
Using precision, accuracy, and reliability with confidence
How to ensure ‘Validity’ of data
Designing alternative experiments to test predictions

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