Looking at data carefully allows you to identify patterns and spot exceptions. This is an important part of analysing data and helps you draw sensible conclusions.

 

 

What Patterns Are

A pattern is a general trend or regular feature in the data.

 

Patterns may include:
• values increasing or decreasing
• repeating results
• clusters of similar values
• a relationship between two variables

 

Patterns help describe what usually happens in the data.

 

For example, data may show that values tend to increase over time or that higher values of one variable are linked with higher values of another.

 

 

Identifying Patterns

To find patterns in data:
• organise the data clearly using tables or graphs
• look for trends rather than individual values
• compare groups or time periods
• consider typical values using averages

 

Patterns are easier to see when data is displayed clearly.

 

Always focus on the overall picture

 

 

What Exceptions Are

An exception is a data value that does not follow the general pattern.

 

Exceptions are also called anomalies or outliers.

 

They may:
• be much larger or smaller than most values
• not fit a trend shown by the rest of the data

 

Exceptions do not necessarily mean the data is wrong.

 

They may represent unusual but real situations.

 

 

Identifying Exceptions

To identify exceptions:
• look for values far from the rest of the data
• check points that do not fit a trend
• compare with typical values such as the median

 

Graphs such as scatter diagrams and box and whisker diagrams make exceptions easier to spot.

 

 

Interpreting Patterns and Exceptions

When interpreting data:
• describe the main pattern first
• then mention any exceptions
• suggest possible reasons for exceptions

 

Exceptions might be caused by:
• unusual events
• measurement errors
• special circumstances

 

You should consider whether exceptions affect conclusions.

 

Do not ignore them without explanation.

 

 

Using Patterns and Exceptions Together

Good data analysis includes:
• identifying patterns to describe typical behaviour
• recognising exceptions that may affect results
• deciding how reliable conclusions are

 

A strong conclusion is based on patterns but also acknowledges exceptions.

 

 

Key Points to Remember

Patterns show general trends or regular features in data.
Exceptions are values that do not fit the pattern.
Data should be organised clearly to reveal patterns.
Exceptions should be identified and discussed.
Both patterns and exceptions affect conclusions.

 

Being able to find patterns and exceptions helps you analyse data critically and draw conclusions that are accurate, balanced and well reasoned.