Proceed only after you have submitted your proposal and received approval, making revisions to your plan as required.
Now that you have gone through and finished all the steps on the Getting Started page, you’re ready to start your experiment.
9. Record your data and observations
As you proceed through your investigation, keep detailed notes of every experiment, trial, measurement, and observation in your journal. This will make your final analysis a much easier task. Make sure you only change only one variable at a time when experimenting and ensure you include control experiments in which none of the variables are changed. It is also important that you include sufficient numbers of test subjects in both control and experimental groups.
A few reminders
Some tips for avoiding a failed investigation:
An experiment may not prove your hypothesis if there were procedural errors, poor or incomplete final analysis or an inaccurate initial hypothesis. Make certain you are recording any sources of error in your journal
i) Procedural Errors –
Now that you have gone through and finished all the steps on the Getting Started page, you’re ready to start your experiment.
9. Record your data and observations
As you proceed through your investigation, keep detailed notes of every experiment, trial, measurement, and observation in your journal. This will make your final analysis a much easier task. Make sure you only change only one variable at a time when experimenting and ensure you include control experiments in which none of the variables are changed. It is also important that you include sufficient numbers of test subjects in both control and experimental groups.
A few reminders
- Keep entering into your journal.
- The more samples or trials you have will result in a much more accurate project.
- In your journal, keep track of anything that might affect the accuracy of your lab (Sources of Error).
Some tips for avoiding a failed investigation:
An experiment may not prove your hypothesis if there were procedural errors, poor or incomplete final analysis or an inaccurate initial hypothesis. Make certain you are recording any sources of error in your journal
i) Procedural Errors –
- It is important that you are very careful and very meticulous with your variables and controls over repeated trials. You must also make sure that you are not working with faulty or damaged equipment and that any equipment used for measurement is properly calibrated
- Poor analysis of results can produce misleading conclusions. Hopefully by submitting a proposal prior to your investigation you have minimized the possibility that there are errors in your design. However, errors in analysis can arise from mathematical errors or from irrelevant data.
- Never manipulate your results to support your initial hypothesis. Your initial hypothesis may have been incorrect or too vague but the experimental results are likely accurate. Find out why your results differed from your hypothesis by explaining unexpected observations. This demonstrates that you understand your topic and the control and handling of variables in repeated trials and value the approach to reaching conclusions.
10. Analyze your data
The purpose of analyzing results is to be able to formulate a sound conclusion.
There are two general categories of analysis:
The presentation of your data should be neat, clear and easily understood. When you complete your experiments, examine and organize your findings. In explaining results and observations, you give meaning to your data. During the initial stages of your investigation, data may have appeared to have little meaning so it is crucial that you organize your data accurately for final analysis and conclusions. Once organized, you can refer to your results as you look for generalizations and conclusions. Data should be organized so that results can be comprehended easily and quickly, possibly by a glance. Some methods for organizing and presenting data include:
The purpose of analyzing results is to be able to formulate a sound conclusion.
There are two general categories of analysis:
- Qualitative Analysis – not based on measurements, based on observation
- Quantitative Analysis – based on measurements, involves numerical data
The presentation of your data should be neat, clear and easily understood. When you complete your experiments, examine and organize your findings. In explaining results and observations, you give meaning to your data. During the initial stages of your investigation, data may have appeared to have little meaning so it is crucial that you organize your data accurately for final analysis and conclusions. Once organized, you can refer to your results as you look for generalizations and conclusions. Data should be organized so that results can be comprehended easily and quickly, possibly by a glance. Some methods for organizing and presenting data include:
Statistical analysis – simple statistics that may be of use are mean, frequency distribution and percentile
Visual representations – diagrams, sketches and pictures can help you convey results in an eye catching way. Other visual representations of data include:
- Mean – The central location of data. Sometimes referred to as average. Calculated by summation of all data numbers divided by the number of data recordings
- Frequency Distribution – Summary of a set of observations demonstrating the number of items in particular categories.
- Percentile – Position of a value from a set of data that expresses the percentage of other data that lie below this value.
- Calculated by listing values in ascending order, divide the percentile you want to find by 100 and multiply by the number of values in the data set
Visual representations – diagrams, sketches and pictures can help you convey results in an eye catching way. Other visual representations of data include:
- Timeline – refers to stages of a project conducted over a period of time with measurements at various stages at specific times
- Flow chart – describes results of a repeated process or sequence in a visually interesting manner.
After you have analyzed your results, consider if your investigation gave you the outcomes you were expecting. Review your work to ensure that the investigation was performed with the exact same steps each time. Make sure you have considered and eliminated all variables that may have impacted results for which you are not testing.
Add generalizations of your results to your mind map
After you have analyzed your data, start your Write Up
Add generalizations of your results to your mind map
After you have analyzed your data, start your Write Up