AC9S6I04: Construct and use appropriate representations, including tables, graphs and visual or physical models, to organise and process data and information and describe patterns, trends and relationships

Learning intention: We are learning to construct suitable tables, graphs and models and use them to describe what scientific evidence shows.

Curriculum focus: Construct and use appropriate representations, including tables, graphs and visual or physical models, to organise and process data and information, and describe patterns, trends and relationships.

Success criteria

• select a table, column graph, line graph or model for a stated scientific purpose
• construct representations with titles, labels, units, even scales and accurate values
• describe patterns, trends and relationships using evidence from the representation
• explain the strengths and limitations of visual and physical models without confusing association with causation

representation

a table, graph, diagram or model used to organise or communicate selected information

data table

an arrangement of values in labelled rows and columns

column graph

a graph using separated columns to compare distinct categories

line graph

a graph commonly used to show change in a measured quantity across continuous time

scale

the numerical intervals on a graph or the stated relationship between model size and real size

trend

the overall direction or form of change across ordered data

pattern

a repeated, grouped or directional feature in data

relationship

a way in which two variables vary together

association

an observed relationship that does not by itself establish cause

model

a simplified representation of selected features of an object, system or process

interpolation

estimating a value between measured data points

extrapolation

estimating beyond the measured data range

Reliable routine

1. Identify the scientific purpose and the type of variables or relationships involved.
2. Select a table, column graph, line graph or model that suits that purpose.
3. Construct it with a descriptive title, precise labels, units, an even scale and a key where needed.
4. Transfer every value or feature accurately and check against the source evidence.
5. Describe patterns, trends and relationships using values and cautious scientific language.
6. Evaluate limitations, including model simplifications, estimates, anomalous data and the difference between association and causation.

Representations have purposes

Use a table to organise and retrieve exact values, a column graph to compare distinct categories, a line graph to show change across continuous time, and a labelled visual or physical model to show selected structures, scales or relationships. More than one representation may be useful when purposes differ.

Build an informative table

Give every column a precise heading and unit. Keep conditions and their results aligned, preserve repeated trials separately, and calculate summaries such as a mean only when useful. Never alter an unusual value merely to create a neat pattern.

Choose graph variables

Place the changed or ordered variable, such as time, on the horizontal axis and the measured outcome on the vertical axis. Category labels belong on a column graph; continuous time data commonly suit a line graph.

Construct graphs accurately

Add a descriptive title, label both axes and units, choose sensible equal numerical intervals, plot every value against the source table and provide a key for multiple series. An unmarked truncated or uneven scale can exaggerate or distort differences.

Describe patterns and trends

Read from left to right and identify increase, decrease, stability, peaks, plateaus, repeated cycles or changes in rate. Support descriptions with values or comparisons and include exceptions rather than forcing all points into a perfect trend.

Describe relationships carefully

Paired data can show that two variables tend to increase together, decrease together or show no clear relationship. This is an association. A graph alone does not prove that one variable caused the other; another factor may influence both.

Use models scientifically

A model represents selected features of a real object, system or process. State its purpose and scale where relevant. Evaluate both what it makes easier to see and what it omits, simplifies or distorts.

Scale and model limits

A larger model is not automatically more accurate. Accuracy depends on whether relevant proportions, positions, relationships and labels match the stated purpose. A solar-system model may scale distance while simplifying planet size, or vice versa.

Compare representations

A table may be best for exact lookup while a graph makes a trend visible and a labelled diagram shows structure. Select or combine forms deliberately, then explain how each feature helps communicate the evidence.

Interpret within the evidence

Interpolation estimates between measured points; extrapolation extends beyond the observed range and is more uncertain. Report what the representation supports, qualify claims to the sample and distinguish direct measurements from estimates and explanations.

• The independent or ordered variable can go on either axis. Conventionally place it on the horizontal axis and the measured outcome on the vertical axis so the relationship is clear.
• Every dataset needs a line graph. Line graphs suit continuous ordered change; distinct categories are usually clearer in a column graph, while tables preserve exact values.
• If two lines rise together, one variable caused the other. The graph shows association only unless an appropriate investigation isolates the possible cause.
• A larger picture is a more accurate model. Model quality depends on accurate relevant features, proportions, scales and labels, not page size.
• Unusual points should be moved onto the trend line. Represent the raw evidence accurately, check unusual values and explain any justified treatment transparently.

• When should I use a table rather than a graph? Use a table when exact values and organised records are the priority; add a graph when comparison or trend needs to be seen quickly.
• Must a scientific graph begin at zero? Not always, but the scale must be even and honest. A shortened axis should be clearly marked and must not be used to exaggerate differences.
• Does a line between points mean every intermediate value was measured? No. Unless those points were recorded, the line supports an interpolation or visual trend, not a direct measurement.
• How do I evaluate a model? State its purpose, name the feature or relationship it represents well, then identify a relevant omission, simplification or scale limitation.
• Can a graph prove cause? Not by itself. Describe the observed relationship and consider controls, study design and alternative factors before making a causal claim.

• Explain why the representation fits the scientific purpose.
• Put the changed or ordered variable on the horizontal axis and the measured outcome on the vertical axis.
• Include titles, labels, units, an even sensible scale, accurate plotting and a key when required.
• Use values to support descriptions of increases, decreases, plateaus, exceptions and relationships.
• Say “is associated with” or “tends to” when the evidence does not establish cause.
• Evaluate models by naming one purpose-specific strength and one meaningful limitation.

• Support: work with one short example, highlighted evidence and a structured response frame.
• Core: complete an unseen example independently and justify the decisive evidence.
• Extend: compare plausible alternatives, explain limitations and create a new example within the Year 6 boundary.

Select and construct a suitable representation for a short dataset, describe its pattern with evidence and state one limit on interpretation.

Evidence of mastery: The student independently selects and constructs accurate tables, graphs and models; describes patterns, trends and relationships from evidence; and evaluates representation limits without treating association as proof of causation.