With any experiment, you have to make compromises to get the type of information that is most important for you. Say you want to test the effects of water and light exposure on plant growth. Seems pretty straightforward, right? So you take some seeds, give them different amounts of water and light, and measure their growth. But wait, what do you mean by growth?
Increases in height, leaf size, circumference, total mass? In this simple experiment, you could collect them all, but this often not practical. Are you interested in changes in the rate of growth? Now you have to decide how you want to change your variables.
If you want to get information about the individual contribution of one variable, you need to hold the other variable constant — so you run two parallel sets of experiments. In one, you give each plant the same amount of water but different amounts of light and vice versa for the other set.
There is always some variability in your variables! For example, there could be genetic differences in the seeds, slight differences in soil composition, differences in distance to the light, etc.
The difficulty of controlling experimental variables is especially pronounced in biology because living organisms are incredibly complex. Replicates: It is impossible to control for every variable — to account for this, scientists include replicates. There are two main types of replicates that are both important:.
Technical replicates are when you test the same sample multiple times to buffer out inconsistencies in measuring. In our plant case, this would mean measuring each plant several times — Were you measuring from exactly the same starting point?
Did you correctly count the number of lines on the ruler? In our plant case, this would mean including multiple seeds in each treatment group. In order to detect effects of your treatment, you need to make sure that differences between treatment groups are bigger than differences between individual samples within those treatment groups, and there are statistical tests scientists use to estimate how likely it is that the effects are due to the treatment.
Controlling variables is crucial, but even if you could perfectly control every variable but the one you are interested in, you would lose important information in doing so. Say you wanted to determine the optimal amount of light and water for plant growth — you change these variables independently as we outlined above, and determine that the optimal amount of light is some value, A, and the optimal amount of water is B.
Many early experiments are performed on cells in a dish cell culture , which allows for moderate control over variables while still working in a cellular context.
If a scientist wants to test the effects of a drug on human cells, they could take cells and plate them in 2 dishes — add the drug to one dish and only the delivery vehicle the liquid the drug is dissolved in to the second dish as a negative control. As we saw above, technical and biological variability could affect the results so the scientist would actually want to set up a number of dishes, not just one of each.
It is exposed to the same conditions as the experimental group, except for the variable being tested. Dependent Variable - A dependent variable is a factor that may change as a result of changes in the independent variable.
Hypothesis - A hypothesis is a tentative explanation for a phenomenon, and is used as a basis for further investigation. It is a specific statement of prediction and describes what you expect to happen in a study.
Independent Variable - An independent variable is a factor that is intentionally varied by the experimenter in order to see if it affects the dependent variable. Population - The group to which the results of an experiment can be generalized. Reliability : Reliability refers to the accuracy and consistency of a measurement or test.
Randomization - Procedure to ensure that every member of a target population has an equal chance of inclusion in a sample. Randomization is necessary to deal with individual differences. Replicate - Replicates are individuals or groups that are exposed to the same conditions in an experiment, including the same level of the independent variable.
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