Being a scientist is a little like being a detective. Scientists look at a problem or a dilemma, and then collect evidence to form a conclusion. Our society relies on scientists to be correct in their conclusions. For example, every year scientists develop a new flu shot to protect us from getting sick. To do this, they have to perform experiments to figure out which strains of the influenza virus are most likely to affect the population that year. Once they have collected enough experimental evidence, they create a flu shot that will provide the most effective protection from those strains. We count on those scientists to form the correct conclusion and develop the right flu shot to protect us all from sickness. But how do they make sure that the answers they find are correct?
This is where experimental design comes into the picture.
Experimental design is all about planning experiments in order to ensure that the results are objective and valid. Since experiments are a fundamental part of scientific discovery, it’s absolutely essential that we design them well. A poorly designed experiment will not provide any evidence to support a particular conclusion, and might even point toward an incorrect conclusion.
Take a look at the graph below, published by the New England Journal of Medicine, which compares the number of Nobel Prizes won to annual chocolate consumption per capita in 20 countries.
The graph shows a clear correlation between the chocolate consumed and Nobel Prizes won. You might look at the evidence provided by this graph and conclude that eating chocolate makes you more likely to win a Nobel Prize, or even that chocolate makes you smarter.
However, the graph fails to take into account other factors that might affect winning a Nobel Prize, such as education standards in each country, or the available research funding. This is a classic example of mistaking correlation for causation. A well-designed experiment would account for those other factors, using positive and negative controls to measure whether chocolate actually influences the likelihood of winning a Nobel Prize.
All scientific research starts with a question that you want to answer. For example, does eating chocolate increase intelligence?
Your hypothesis is a testable explanation of the research question. The experiment you design will give evidence that either supports or refutes that hypothesis. Be careful not to confuse hypotheses with predictions. A prediction is what you think will happen in the future, while a hypothesis is a possible explanation of what is happening now.
A hypothesis could be something like: Eating two ounces of chocolate per day increases IQ.
The experimental model is the technique and sample you use to test your hypothesis. In other words, what tests you’ll perform, and what test subjects you’ll use. Some experiments use plates of cells, some use mice, some use computer programs, and others use humans. It all depends on what you’re testing!
There are many experimental models, and you should try to choose the most effective one for your experiment.
Once you have an experimental model to test your hypothesis, you need to define which variable you want to measure. In an experiment, there is generally a dependent variable and an independent variable. Independent variables are controlled by the experimenter. Dependent variables are what you measure in response to changes in the independent variable. In our example, the independent variable is the amount of chocolate consumed, and the dependent variable is the measured change in IQ.
Experimental controls allow you to be more certain that the independent variable is responsible for the changes in the dependent variable. There are two kinds of controls: you expect a known response in positive controls, and expect no response in negative controls. A positive control would have to be a treatment that is known to increase IQ, such as proven brain training exercises, while a negative control would be a lack of treatment altogether.
Now it’s time to experiment! Run your tests on the test groups, positive controls, and negative controls, and record the results.
Look at the resulting data and analyze it. Does your data support your hypothesis, or not?
Once you have carried out your experiment with controls, you will have an idea of whether or not your hypothesis is correct – but that still isn’t proof! We can only make conclusions if the experiment is reproducible and the results are consistent over several replications. Once you have performed the experiment several times and observed similar results, then you can analyze the data and start to draw conclusions.
The best way to learn how to design a scientific experiment is to try it yourself. In the Experimental Design simulation, you will step into the shoes of a pharmaceutical detective for a day. Your task is to design and carry out an effective experiment to find out if a new drug is causing damage to kidney cells.
Here’s a brief overview of the topics you’ll cover in the simulation below.
Humans have always strived to explain natural phenomena. In the first mission of the experimental design simulation, you will do just that. The scientific method gives you a practical template for forming and testing conclusions. Using it, you can investigate phenomena, acquire new knowledge, or correct and integrate existing knowledge surrounding your experiment.
In your next mission, you have the freedom to design your own experiment from scratch while examining human kidney cells under a microscope. Every tool you need to design and perform your experiment is available in this virtual lab.
Choose an experimental model: Choose the correct experimental model from yeast cells, mice, human samples, or kidney cell lines, to design a scientifically sound experiment for testing the effect of the suspicious drug. And don’t be afraid to make mistakes – in this virtual simulation you can repeat the experiment as many times as you want.
Define the dependent and independent variables: Based on your experimental model, which variable will you be manipulating as the experimenter? What will you be measuring?
Create controls: The positive and negative controls will help you conclude whether a suspicious compound could be the reason behind the reported epidemic. You’ll get to investigate the effects of compounds in the drug as compared to a negative control with just the buffer, and a positive control treated with a compound known to cause kidney damage.
Perform the experiment: After adding the treatments to the positive control, negative control, and experimental group, use the lab equipment to observe the changes in the dependent variable in each experimental group.
Analyze the results and present your findings: Look at your data, and decide whether or not it supports your hypothesis. Then present the results of your experiment at a meeting with a virtual professor.
Are you ready to set up your own experiment and test the side effects of a suspicious new drug? Click the button below!
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