
Qualitative Data: This refers to non-numerical information, such as descriptions or observations. For example, noting the colour change in a chemical reaction or recording behaviours in animals.
Quantitative Data: This involves numerical data, such as measurements or counts. For instance, measuring the temperature over time during an experiment or counting the number of seeds that germinate. |
Critically Analyse Data:
Identify Anomalous Observations:
Draw and Justify Conclusions:
By following these steps, students not only learn to collect and interpret data but also develop critical thinking skills by analysing and evaluating the results. This prepares them to draw logical, evidence-based conclusions, an essential part of scientific inquiry.
- After collecting data, it needs to be carefully analysed. This involves looking for patterns, trends, or relationships between variables. For example, you might notice that increasing the temperature leads to faster growth in plants.
- Identifying Patterns and Relationships: Look for consistent results or trends that suggest how one variable affects another. This is key in understanding cause-and-effect relationships in science.
Identify Anomalous Observations:
- Sometimes, results do not fit the expected pattern. These unusual results are called anomalies. It’s important to note and investigate them because they might suggest an error in the experiment or lead to new insights.
Draw and Justify Conclusions:
- Based on the data and analysis, students should draw conclusions. This means summarising what the results indicate in relation to the original hypothesis.
- Justify Conclusions: The conclusions should be backed by the evidence gathered. This involves explaining why the data supports (or does not support) the hypothesis, citing specific results to justify your reasoning.
By following these steps, students not only learn to collect and interpret data but also develop critical thinking skills by analysing and evaluating the results. This prepares them to draw logical, evidence-based conclusions, an essential part of scientific inquiry.