To determine how Rajesh can improve the validity of his experimental data, let's evaluate each of the suggested changes one by one:
1. Add twice as much fertilizer to the plants.
- This change is not necessary since the aim of the experiment is to observe the effect of fertilizer at different levels, not necessarily to use the maximum amount of fertilizer. Therefore, this would not improve the validity of the data.
2. Measure the growth of a plant that did not receive fertilizer.
- Including a control group (plants that receive no fertilizer) is crucial to establish a baseline comparison. This will allow Rajesh to determine the growth of plants under normal conditions and compare it efficiently against the plants that received fertilizer. Including a control group is a well-established scientific practice to improve the validity of experimental data.
3. Perform additional trials.
- Conducting more trials can enhance the reliability of the results. A larger sample size can help in reducing the effect of random variations and increase the accuracy of the conclusions drawn. Repetition is a fundamental principle in scientific experiments to confirm the consistency of the results.
4. Remove plant 2.
- Removing Plant 2 without a valid, scientific reason (such as it being an outlier due to experimental error or other factors not related to the scope of the experiment) can introduce bias into the data. Hence, removing Plant 2 is not recommended unless there's a clear justification.
Based on the above evaluations, Rajesh should make the following changes to enhance the validity of his experimental data:
- Measure the growth of a plant that did not receive fertilizer (i.e., include a control group).
- Perform additional trials.
In conclusion, Rajesh should implement changes 2 and 3 to improve the validity of his experimental results.
