Summarize Results Making sense of and communicating your experimental results is important but can be tricky. Tables and graphs are good tools for determining whether your experimental results support or refute your hypothesis, by letting you see which of your predictions were correct. There is no single right way to do this! If you need guidance, look back at your predictions, and consider. What did you manipulate and what did you measure? Click here for explanations of variables.
To summarize your results:
- Use the selectors on the right to explore your recorded data. Click /Launch Notepad for an alternative view.
- Decide on a table that is the best choice (of those possible) for summarizing your results, and then click Get Feedback for feedback on your choices.
- To communicate your results to others: On paper or in a spreadsheet, construct a summary graph or formatted table. Click Export Data to save a text file that can be opened in a spreadsheet application. After summarizing your results, continue on to write a conclusion (Then you can design more experiments!)
The Correct Answer and Explanation is:
Correct Answer:
To summarize your results, construct a summary table or graph that best displays the relationship between the variable you manipulated (independent variable) and the variable you measured (dependent variable). For instance, if you changed temperature and measured reaction rate, your table or graph should clearly show how different temperature values correspond to different rates of reaction. Choose the table or graph that most clearly shows trends, patterns, or anomalies in your data. Then, write a brief narrative explaining whether your data supports or refutes your original hypothesis and why.
Explanation
Summarizing experimental results is a critical step in the scientific method, as it allows you to evaluate whether your predictions were accurate and to draw logical conclusions based on evidence. A well-organized table or graph helps you and others clearly visualize how the dependent variable responded to changes in the independent variable. Tables are great for presenting exact data values, while graphs (like bar charts, line graphs, or scatter plots) are ideal for spotting trends, relationships, and outliers.
Start by revisiting your hypothesis and identifying what variable you changed (independent variable) and what you measured (dependent variable). For example, if your hypothesis predicted that increased light would make plants grow faster, then light intensity is your independent variable, and plant growth (height, number of leaves, etc.) is your dependent variable. In this case, a line graph with light intensity on the x-axis and plant height on the y-axis would visually show the trend and support conclusions.
Next, select the most informative way to present your data using the tools provided (e.g., notepad, data explorer, summary graph, or export options). Consider what makes the pattern clearest: numerical trends, comparisons, or cause-effect relationships.
After creating your summary graph or table, interpret your findings. Did the data support your hypothesis? Were there unexpected results? This analysis becomes the foundation for your conclusion and can guide further experimentation. Accurate summarization bridges raw data and scientific insight, making your experiment meaningful and reproducible.
