Year 8 Science · Unit 4 · Lesson 7

Identifying and Handling Outliers

Foundation Worksheet

Name

Date

Class

Learning Goals

Define an outlier and list common causes such as measurement error, recording mistakes, and genuine rare events.
Identify potential outliers in a dataset by comparing values and looking for unusual results.
Explain why outliers should never be silently deleted and describe the correct steps for handling them.

Warm Up

Sort it!

Write each description into the correct category box — Likely outlier or Not an outlier. Include a short reason after each one.

All 5 temperature readings were 22°C except one reading of 220°C Reaction took 25 s for most trials but 27 s for one trial pH readings: 7.1, 7.0, 7.2, 7.1, 0.7 Plant heights: 12 cm, 13 cm, 11 cm, 12 cm, 13 cm Dissolving times: 40 s, 42 s, 39 s, 41 s, 410 s Five masses: 50.1 g, 50.0 g, 50.2 g, 50.1 g, 50.3 g Pendulum swing: 1.9 s, 2.0 s, 2.1 s, 2.0 s, 9.8 s Speeds: 8 m/s, 9 m/s, 8 m/s, 8 m/s, 8 m/s

Likely outlier

Not an outlier

Your Turn

True or False? Fix the false ones

Circle T or F for each statement. If the statement is false, rewrite it correctly on the line below.

If a data point does not fit the trend, you should delete it from your results immediately.

Correct it:

An outlier always means you made a measurement error.

Correct it:

Recording anomalous results in your data table is optional if they make your graph look messy.

Correct it:

Repeating a measurement that looks odd is good scientific practice.

Correct it:

Show What You Know

1. In your own words, define an outlier and give one example of a cause that is a measurement error and one cause that could be a genuine scientific result.

Recall 2 marks

2. A student finds a result that looks unusual in their data. Write two questions they should ask themselves before deciding what to do with it.

Recall 2 marks

Wrap Up

In one sentence, what was the main idea of this lesson?