Two important elements of The Scientific Method that will help you design your research approach more efficiently are “Generating Hypotheses” and “Designing Controlled Experiments” to test these hypotheses. A well-designed experiment that you deeply understand will save time and resources and facilitate easier data analysis/interpretation. Many people reading this may be working on a project that focuses on designing a product, or discovery research where the hypothesis it is not immediately obvious. We encourage you to read on however as the exercise of generating a hypothesis will likely help you think about the assumptions you are making in your research and the physical principles your work builds upon. Show
These activities will help you …
What is a Hypothesis?A hypothesis is an “educated guess/prediction” or “proposed explanation” of how a system will behave based on the available evidence. A hypothesis is a starting point for further investigation and testing because a hypothesis makes a prediction about the behavior of a measurable outcome of an experiment. A hypothesis should be:
A useful hypothesis may relate to the underlying question of your research. For example: “We hypothesize that therapy resistant cell populations will be enriched in hypoxic microenvironments. “ “We hypothesize that increasing the number of boreholes simulated in 3D geological models minimizes the variation of the geological model results.” Some research projects do not have an obvious hypothesis to test, but the design strategy/concept chosen is based on an underlying assumption about how the system being designed works (i.e. the hypothesis). For example: “We hypothesize that decreasing the baking temperature of the photoresist layer will reduce thermal expansion and device cracking” In this case the researcher is troubleshooting poor device quality and is proposing to vary different fabrication parameters (one being baking temperature). Understanding the assumptions (working hypotheses) of why different variables might improve device quality is useful as it provides a basis to prioritize what variables to focus on first. The core goal of this research is not to test a specific hypothesis, but using the scientific method to troubleshoot a design challenge will enable the researcher to understand the parameters that control the behavior of different designs and to identify a design that is successful more efficiently. In all the examples above, the hypothesis helps to guide the design of a useful and interpretable experiment with appropriate controls that rule out alternative explanations of the experimental observation. Hypotheses are therefore likely essential and useful parts of all research projects. Suggested Activity – Create a Hypothesis for Your ResearchEstimated time: 30 mins
Remember that writing a good research hypothesis is challenging and will take a lot of careful thought about the underlying science that governs your system. Designing ExperimentsDesigning experiments appropriately is very important to avoid wasting resources (time!) and to ensure results can be interpreted correctly. It is often very useful to discuss the design of your planned experiments in your meetings with your supervisor to get feedback before you start doing experiments. This will also ensure you and your supervisor have a consistent understanding of experimental design and that all the appropriate controls required to interpret your data have been considered. The factors that must be considered when you design experiments is going to depend on your specific area of research. Some important things to think about when designing experiments include: Rationale: What is the purpose of this experiment? Is this the best experiment I can do? Does my experiment answer any question? Does this experiment help answer the question I am trying to ask? What hypothesis am I trying to test? Will my experiment be interpretable? What controls can I use to distinguish my results from other potential explanations? Can I add a control to distinguish between explanations? Can I add a control to further test my hypothesis? Is my experiment/model rigorous? What is the sensitivity of the method I am using and can it measure accurately what I want to measure? What outcomes (metrics) will I measure and is this measurement appropriate? How many replicates (technical replicates versus independent replicates) will I do? Am I only changing the variable that I am testing? What am I keeping constant? What statistical tests do I plan to carry out and what considerations are needed? Is my statistical design appropriate (power analysis, sufficient replicates)? What logistics do I need to consider? Are the equipment/resources I need available? Do I need additional training or equipment access? Are there important safety or ethical issues/permits to consider? Are pilot experiments needed to assess feasibility and what would these be? What is my planned experimental protocol and are there important timing issues to consider? What experimental outputs and parameters need to be documented throughout experiment? This list is not exhaustive and you should consider what is missing for your particular situation. Suggested Activity – Design an Experiment Using a TemplateEstimated time: 45 min
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