Designing Physics Experiments
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All physics students will have to design experiments at some point in our school lives. Some of us will have to complete in-depth studies, core practicals, or internal assessments that make up large chunks of our final grades, whilst others will have to write exam papers with experimental design questions incorporated into them. Designing experiments help illustrate our knowledge and understanding of the scientific method and can often provide an excellent opportunity to improve our insight into a scientific phenomenon! Here are a few things to keep in mind when embarking on such a task.
Come Up with a Question
Designing an experiment has to start with a precise and focused question which can be clearly answered within the set parameters, such as time constraints or types of equipment available. I know we would all love to have a go at conducting an experiment with a particle accelerator, but for now, there is a small chance of this actually happening. So what’s first? Start with an area in Physics that you are exploring or are interested in, and read! Reviewing previous research is the cornerstone of a well-developed research question: look for the different relationships between variables, the various applications and consequences of the findings. Find areas that could be potentially tested in a school lab. Our aim is to whittle down the plethora of available information, so that we can explore the relationship of two variables in a reliable, precise and accurate manner. We should pick something that is interesting- we should talk honestly about why the topic is interesting and what uses does it have in the world. These considerations usually form the introduction and background information of a report, so we should talk about all the relevant physics theory involved here. Using scientific theories we have learned from our research, we should end this section with a testable question that may look something like “How does [Independent Variable] affect [Dependent Variable]?”
Once we have researched an area in physics and come up with our testable question, we should try and predict the most likely outcome of the investigation based on our understanding of the theories. The important bit is to actually justify what physics knowledge is being used to come up with the initial prediction. Whilst a goal of any investigation is to test the validity of its hypothesis, remember it is ok to get the prediction wrong. As scientists we care far more about why things went wrong and how we can explain the new finding rather than just being wrong.
Do the experiment!
Now that we have a research question which identifies our variables clearly, we must design a method for the data collection which is feasible in our school laboratories. So, go play with the equipment available! Whatever variables are indicated by our theory, in this stage of our investigation we must find a way to quantify them. For example, we must pick reasonable control variables and measurable intervals for our independent variable. We can’t do this until we have conducted a pretest: played with the equipment. It is a good idea to produce a variable table from the pretest which clearly indicates which variables need measuring and calculating, and how we plan on doing this. This should help us write up our equipment list, and we should make a note of the uncertainties in our measured quantities due to the precision of the measuring tools. The key here is to justify your choices: the three magic words to keep in mind are precision, reliability, and accuracy. If there are quantities which are given to you, for example, the mass of a projectile is provided for you, then also record these and indicate whether uncertainties were provided by manufacturers. Once we have decided on how we are going to answer the question, we must conduct our experiment and collect results.
Results! Results! Results!
Once we have results, we must actually answer the question. Things can go wrong in even the most simple and familiar experiments, and it’s ok for things to go wrong! We can’t just brush these events under the carpet though: if time permits, repeat anomalies when you can, otherwise just indicate which results are anomalous. If during data collection, we had noticed flaws in our method and had made adjustments, then talk about them. The rule of thumb here is to write about everything you actually did, and to justify deviations from the original design where appropriate. Some of the greatest scientific discoveries were initially thought to be anomalous results, so don’t lie about yours! Here we must remember to analyze our results: this means we should comment on the validity of our hypothesis, compare our results to literature values if possible, comment on the combined uncertainty in our result. Standard deviations, correlation coefficients, and other statistical methods can be used to comment on our results: we should explain which ones we choose to use and show how we calculated them.
How’d it go?
What went wrong? What worked? If we had unlimited time to work on this experiment, what would we do next and why? These are the final three main questions we must answer to evaluate our investigation. If things didn’t go as planned or our results didn’t validate the hypothesis, this is where we should try and explain what we think went wrong. If our hypothesis was not valid then we must comment on the suitability of the method, equipment, and the analysis of the results: talk about the assumptions we made in selecting our method and equipment. We can always conduct 100 more trials to make our results more reliable but this will only test the reliability of our method, but not any systematic errors. To improve the reliability of the method, we must discuss improvements to the method that will reduce random errors. Averaging results and using lines of best fits can help further improve the reliability of an experiment: we should comment on if we omitted anomalous results from mean calculations, methods of drawing the lines of best fit, and regression results. Why would a method be more accurate or more precise, and which anomalous results might we be able to eliminate as a result? For improving the accuracy of our results, we should discuss how to reduce systematic errors: keep this in mind when suggesting the uses of different equipment. When proposing improvements or alternate methods, remember to explain the benefits and how these changes will affect the result.
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