Introduction: The "So What?" of Science
You’ve finished your experiment, you’ve collected a pile of numbers, and you’ve drawn a beautiful graph. Now comes the most important part: Conclusions and Evaluation. This is where you explain what your results actually mean and look back at your work to see if you can really trust it. Think of this as being a judge in a courtroom—you are looking at the evidence to see if it proves your case!
1. Drawing Valid Conclusions
A conclusion is a statement that describes the relationship between the variables you investigated. To be a valid conclusion, it must be supported by the data you collected and nothing else.
Finding Patterns
Look at your table or graph. Is there a clear pattern?
• Positive correlation: As one thing goes up, the other goes up (e.g., as light intensity increases, the rate of photosynthesis increases).
• Negative correlation: As one thing goes up, the other goes down (e.g., as temperature increases above the optimum, the rate of enzyme activity decreases).
• No correlation: The points are scattered and don't show a clear link.
Linking to Science
In your exams, you won't just be asked what happened; you’ll be asked why. A top-tier conclusion uses scientific knowledge to explain the pattern.
Example: "The rate of respiration increased because exercise requires more energy, which is released when \(C_6H_{12}O_6\) reacts with \(6O_2\)."
Quick Tip: Don't just say "The graph goes up." Use the names of your variables! Say "As the independent variable increases, the dependent variable also increases."
2. Evaluation: Checking Your Data Quality
Before you celebrate, you need to be honest: how good was your data? This is called evaluation.
Spotting Anomalies
An anomaly is a result that does not fit the pattern of the others.
• What to do: If you see an anomaly, you should ignore it when calculating your mean (average).
• Why do they happen? Usually because of a mistake during that specific measurement, like starting the stopwatch too late.
Accuracy vs. Precision
These two words sound the same, but in biology, they are very different!
• Precision: How close your repeated measurements are to each other. If you measured the same thing three times and got \(10.1\), \(10.2\), and \(10.1\), your results are precise.
• Accuracy: How close your measurement is to the true value. You could be precise (getting the same number) but inaccurate if your equipment is broken!
Understanding Errors
Errors aren't always "mistakes"—sometimes they are just part of the equipment.
• Random Errors: These are unpredictable. They happen because of things like human reaction time or slight changes in the room temperature. You can reduce their effect by taking at least three readings and calculating a mean.
• Systematic Errors: These happen every time you take a measurement. A common one is a zero error, where a balance shows \(0.1g\) even when nothing is on it. These affect the accuracy of your results.
Key Takeaway: Always look for anomalies and calculate a mean to make your results more repeatable!
3. Evaluation: Reliability and Validity
The exam board has a specific rule: stop using the word "reliable"! Instead, use these two specific terms:
Repeatability and Reproducibility
• Repeatable: If you do the experiment again using the same method and equipment, do you get the same results?
• Reproducible: If someone else does the experiment (or you use different equipment/techniques), are the results still the same?
Validity
An experiment is valid if it actually tests what it set out to test. To be valid, you must keep all control variables the same.
Example: If you are testing how light affects photosynthesis but the temperature keeps changing, your experiment is not valid because you don't know which factor caused the change.
Analogy: Imagine trying to see who is the fastest runner, but one person is wearing running shoes and the other is wearing heavy boots. The test isn't valid because it isn't a fair test!
4. Suggesting Improvements
In the final part of an evaluation, you need to suggest how to make the experiment better. Don't worry if your experiment wasn't perfect; scientists suggest improvements all the time!
Common Improvements to Suggest:
• Use a wider range: Test more values (e.g., test temperatures from \(0^{\circ}C\) to \(100^{\circ}C\), not just \(20^{\circ}C\) to \(40^{\circ}C\)).
• Use smaller intervals: Instead of testing every \(10^{\circ}C\), test every \(2^{\circ}C\) to find a more precise optimum temperature.
• Better equipment: Use a gas syringe instead of counting bubbles to get a more precise volume.
• Control more variables: Use a water bath to keep the temperature exactly the same.
Did you know? Using a computer data logger is often suggested as an improvement because it can take measurements very frequently and removes human error in timing!
Quick Review Checklist
Before an exam, ask yourself these questions about any practical:
• Did I identify the pattern in the results?
• Did I use science words (like enzymes, diffusion, or respiration) to explain it?
• Did I find any anomalies and leave them out of my average?
• Is my method valid (were the controls actually controlled)?
• How could I make the measurements more precise? (e.g., using a ruler with \(mm\) instead of \(cm\)).
Note: For more details on how to set up these experiments, see the chapter on "Required Practicals".