Planning
Planning
Identifying Variables
- Distinguish between independent, dependent, and control variables.
- The independent variable is the one you change in the experiment.
- The dependent variable is what you measure in the experiment.
- Control variables are all other factors which must be kept constant to ensure a fair test.
Formulating a Hypothesis
- A hypothesis is a scientific prediction about the expected outcome of an experiment.
- The hypothesis should be based on prior scientific knowledge.
- Include in it a scientific explanation that indicates an understanding of the physics relevant to the experiment.
Estimating Results
- An initial estimate of results can help identify potential problems in data collection before beginning the full experiment.
- This step also allows for an assessment of the range and spread of data which can guide decision making with regard to uncertainty analysis.
Safety Considerations
- Before starting an experiment, you should always consider any potential risks associated with the procedure.
- Ensure to reduce the risk of hazard in order to protect yourself and others.
- Organize your data collection strategy to reduce errors and ensure efficiency.
Practical Techniques
- Accurate data collection is essential. This often involves using suitable equipment and technical skills.
- Important techniques include correctly setting up equipment, taking accurate measurements, and repeating readings to estimate random uncertainties.
- You should consider any limitations or potential sources of error that may affect the quality of your data (e.g. parallax error).
- Keep good records of each step and the results, including diagrams of the setup and tables of observations.
Data Analysis
- Always review the data to identify any unexpected results or mistakes in data recording.
- Graphical methods can be utilised to present and interpret data.
- Calculate means and ranges of values, and consider sources of uncertainty and how they can affect the analysis.
- Conclusions should be clearly stated and justified with evidence from the data.
Evaluation of Method
- Critically evaluate your method to recognise areas that could be improved in the future.
- Identify systematic errors and explain how they could be minimised.
- Discuss how data treatments could be modified to reduce uncertainty.
- Reflect on the implications of the uncertainty upon the conclusions drawn.