Science · Year 6 · Working Scientifically: The Grand Investigation · Summer Term

Analyzing Results and Drawing Conclusions

Interpreting data, identifying patterns, and drawing conclusions based on evidence.

• National Curriculum Attainment Targets
• KS2: Science - Working scientifically

About This Topic

Analyzing results and drawing conclusions forms a core part of Working Scientifically in Year 6. Students interpret data from experiments, such as those on forces, light, or living things, to identify patterns and trends. They learn to justify conclusions with evidence and distinguish observations, which are direct sensory descriptions, from inferences, which are explanations based on that evidence. This skill directly supports the National Curriculum standards for KS2, where pupils report findings using scientific language and consider whether results support hypotheses.

These practices connect across science units, reinforcing skills like fair testing and variable control from earlier investigations. Students develop critical thinking by evaluating data reliability, such as outliers or repeated trials, and communicating findings through graphs, tables, and discussions. This prepares them for secondary science, where evidence-based reasoning underpins all inquiry.

Active learning suits this topic well. When students collaborate to analyze shared datasets or debate inferences from class experiments, they practice articulating evidence and refining ideas through peer feedback. Hands-on data handling with real or simulated results makes abstract analysis concrete and builds confidence in scientific argumentation.

Key Questions

1. Analyze data to identify patterns and trends.
2. Justify conclusions using evidence from experimental results.
3. Differentiate between observation and inference in scientific inquiry.

Learning Objectives

• Analyze experimental data presented in tables and graphs to identify patterns and trends related to scientific investigations.
• Justify conclusions drawn from experimental results by referencing specific pieces of evidence.
• Differentiate between direct observations and inferences made during a scientific investigation.
• Evaluate the reliability of experimental data, considering potential sources of error or outliers.
• Communicate findings and conclusions clearly using appropriate scientific vocabulary and representations.

Before You Start

Collecting Data

Why: Students need to have experience gathering information through observation and measurement before they can analyze it.

Fair Testing and Variable Control

Why: Understanding how to conduct a fair test is essential for producing reliable data that can be meaningfully analyzed.

Representing Data (Tables and Graphs)

Why: Students must be able to organize and visualize data in tables and simple graphs to identify patterns and trends effectively.

Key Vocabulary

Data	Information collected during an experiment, often in the form of numbers, measurements, or observations.
Pattern	A recurring characteristic or event observed in data that suggests a relationship or trend.
Trend	A general direction in which data is changing over time or across different conditions.
Conclusion	A summary or judgment reached after considering all the evidence from an investigation.
Inference	An explanation or interpretation of an observation based on prior knowledge or reasoning.
Evidence	Facts or information indicating whether a belief or proposition is true or valid, used to support conclusions.

Watch Out for These Misconceptions

Common Misconception

Conclusions are just guesses without data.

What to Teach Instead

Emphasize that valid conclusions rest on evidence from results. Active group discussions of sample datasets help students see how patterns support claims, reducing reliance on opinion. Role-playing as scientists defending conclusions builds this habit.

Common Misconception

Correlation means causation.

What to Teach Instead

Patterns in data do not prove one variable causes another. Hands-on sorting of real experiment cards into correlation/causation categories clarifies this. Peer teaching reinforces evaluation of controls and repeats.

Common Misconception

Observations and inferences are the same.

What to Teach Instead

Observations describe what is seen; inferences explain why. Matching games with experiment photos separate the two effectively. Collaborative sorting activities help students internalize the distinction through trial and error.

Active Learning Ideas

Stations Rotation: Data Analysis Stations

Prepare stations with datasets from past experiments: one for graphing rainfall trends, one for spotting patterns in plant growth, one for identifying outliers in pendulum swings, and one for writing conclusions. Groups rotate every 10 minutes, recording patterns and evidence-based claims at each. Conclude with a whole-class share-out.

45 min·Small Groups

Pairs Debate: Observation vs Inference

Provide photos or experiment videos. Pairs list three observations then propose inferences, swapping with another pair to critique using evidence criteria. Facilitate a class vote on strongest inferences. Extend by applying to student-collected data.

30 min·Pairs

Whole Class: Trend Hunt Gallery Walk

Display graphs and tables from unit investigations around the room. Students walk in pairs, noting patterns and drafting conclusions on sticky notes. Regroup to cluster notes and vote on class conclusions.

35 min·Pairs

Individual: Evidence Journal

Students review personal experiment logs, highlight data patterns, and write justified conclusions. Peer review follows, focusing on evidence links.

25 min·Individual

Real-World Connections

• Medical researchers analyze patient data from clinical trials to determine if a new drug is effective and safe, drawing conclusions about its benefits and risks.
• Environmental scientists collect data on air and water quality in different regions to identify pollution trends and draw conclusions about the impact of human activities on ecosystems.
• Engineers analyze test results from prototypes to identify design flaws and draw conclusions about necessary modifications before mass production of a new product.

Assessment Ideas

Exit Ticket

Provide students with a simple data table from a completed experiment (e.g., plant growth under different light conditions). Ask them to write one sentence identifying a pattern in the data and one sentence stating a conclusion based on that pattern.

Discussion Prompt

Present students with a set of observations from a simulated investigation (e.g., a ball rolling down a ramp). Ask: 'What did you directly observe?' and 'What is one inference you can make based on your observations? How does your inference connect to the evidence?'

Quick Check

Show students a graph displaying experimental results. Ask them to point to the part of the graph that shows a trend and explain in their own words what that trend means. Check for understanding of how the visual representation relates to the data.

Frequently Asked Questions

How do you teach Year 6 students to distinguish observation from inference?

Start with concrete examples from experiments, like 'the bulb lit' as observation and 'because the circuit is complete' as inference. Use sorting cards with student-generated data for pairs to categorize, then discuss in plenary. This builds precision in scientific language and prepares for reporting findings.

What active learning strategies help with analyzing data patterns?

Station rotations with varied datasets encourage hands-on graphing and trend spotting in small groups. Gallery walks let students critique peers' analyses, fostering deeper pattern recognition. These approaches make data interactive, helping students link observations to trends through collaboration and movement.

How can teachers address misconceptions in drawing conclusions?

Explicitly model evidence justification using think-alouds on class data. Provide misconception checklists for self-assessment during group work. Peer debates on sample conclusions correct errors like assuming causation, as students defend claims with evidence and learn from counterarguments.

What evidence-based conclusions look like in Year 6 science?

Strong conclusions state patterns from data, link to hypotheses, and note limitations like sample size. For example, 'Three trials show mass affects pendulum speed, supporting our prediction, though wind may have influenced one result.' Practice through scaffolded templates transitions to independent writing.