As an early part of our study of a science, we need to all be "on the same page" when it comes to a few terms. We can define science as a methodical approach gaining knowledge. This important word distinguishes how a scientist works from how other people learn about the world. Science is an approach that is methodical, and that approach helps acquire knowledge. Science is not the knowledge gained through the approach. Knowledge can be gained through a variety of ways, but science acquires knowledge methodically.
The method of science is a pathway that involves several steps. Scientists themselves might organize the pathway in slightly different ways, but scientists would agree that what is presented here is one format of the scientific method.
The scientific method is based upon evidence rather than belief. This distinguishes science from faith. A scientist is suitably skeptical of anything but good evidence. That is not to say that scientists lack faith...it is just that faith for them operates in a different sphere of their lives. In scientific work there is little room for faith; in life there is plenty of room for both.
The scientific method, then, is founded upon direct observation of the world around us. A scientist looks critically and attempts to avoid all sources of bias in this observation. But more than looking, a scientist measures to quantify the observations; this helps in avoiding bias.
The system of measurements used in this observation part of the scientific method is the metric system.
The next part of our scientific method is to form a hypothesis. A hypothesis is a natural explanation of an observation. In other words, after we notice something in the world, we hypothesize, or try to explain how or why it is true.
You examine the literature on the subject; scientists need libraries, reading is critical to scientific performance! You gather as much book knowledge as you can on the subject to begin to arrive at an explanation of your observation. This tentative explanation is your hypothesis.
Please notice that hypotheses do not always have to be correct. In fact most of science is spent trying to determine the validity of a hypothesis, yet this effort is NOT likely to give a single perfect answer. So, in formulating your hypothesis, you should not worry too much that you have come up with the best or the only possible hypothesis. The rest of the scientific method will test your hypothesis. What will be important is your decision at the end of the method.
The one aspect of your hypothesis is important, though. It really must be rejectable or falsifiable. There must be a way to test the possible answer to try to make it fail. If you design an untestable hypothesis, then science cannot be used to help you decide if it is right or not. For the moment, let us say that your hypothesis is "God needs water to survive." There is no way to test the survival of God scientifically. Switch the word God to, for argument, beetles, and the hypothesis is testable.
The prediction is a formal way to put a hypothesis to a test. If you have carefully designed your hypothesis to be sure it is falsifiable, then you know precisely what to predict. The prediction has three parts:
- If my hypothesis is true,
- then _____ should happen (or change),
- when _____ is manipulated
The manipulation is what you knew would likely falsify your hypothesis.
- If beetles need water...
- Then most of them will die
- When I take water away from them.
The blanks in the generic format for the prediction above, represent what are called two variables. The first blank above is for the dependent variable and the second blank above is for the independent variable. The independent variable is the one that you manipulate and the dependent variable is the response that you measure. In other words, the independent variable is the suspected cause while the dependent variable is the suspected effect. The word "suspected" is appropriate because the experiment is testing if the cause-effect relationship is true. So in our example, the independent variable is the amount or presence of water...and the dependent variable is the life of the beetles.
This part of the scientific method is the key to testing the hypothesis. If this prediction holds then you will not be able to reject your hypothesis. If this prediction does not hold, then you will reject your hypothesis.
Rejecting the hypothesis is usually the desired outcome as we shall see...
This is the actual hands-on part of the project. Here you carry out your manipulation and compare the results with results from a control setting. This may entail setting up an experimental group and a control group. The experimental group are the test subjects who receive abnormal or special treatment. The control group are the test subjects that receive normal treatment. Perhaps we grow a large group of beetles (our test subjects.) In the control group, the beetles are given water—just like they would normally be given. In the experimental group, the beetles are not given water—a very abnormal occurrence. The experimenter would keep track of how long the beetles survived. It is important that the only difference between these groups is the availability of water; all of the beetles must be of the same species, same age, same diet, same location, same everything except water availability. These things that are kept the same for all of the test subjects (control and experimental groups) are called constants. If there is any difference other than water availability, the experimenter will not be able to conclude that the lack of water caused the death of the beetles. For example, let’s say that the experimental group of beetles did not get water and were older than the control beetles. If the experimental beetles did die before the control beetles, then we won’t know if the lack of water or the older age led to the death of these beetles. Lesson learned: experimental and control groups must be large in number and almost identical to each other except for the one manipulation/independent variable.
To be an experiment, we must compare the results of some manipulation (i.e, no water) with the results of an unmanipulated (control) situation (i.e., water.) Not everything we do in science compares a treatment with a control, but most useful information is derived from experimental science.
How do we compare the results? As good scientists we will try to repeat (replicate) our experimental treatments several times to avoid chance error. But once we repeat, we may get a mixture of "positive" results and "negative" results. How will we know which results are typical or correct?
There are many sources for error. Some of the beetles were sick and were going to die anyway—with or without water. Perhaps we handled some of the beetles differently. Perhaps some of the beetles were placed in a colder box, which caused their death. For all of the reasons above, we must use large experimental and control groups, repeat our experiment, and average our results.
Typically, the data has to be organized into data tables and displayed in graphs. A scientist would have to think about the most effective way to organize and display the data. To ensure that the scientist is clear, all tables and graphs should have titles and labels and units must be included.
Here we make up our minds by estimating our error and analyzing our data. We have two options: "reject the hypothesis" or "cannot reject the hypothesis" and only these two options!
Please notice that we do not prove hypotheses! Proof exists when the chance for error is 0. There is always some chance for error (no matter how small it is!) and this existence of chance error means we cannot prove anything in true, honest, science. Either the data supports or does not support the hypothesis.
Should a scientist worry when the hypothesis is rejected? Certainly not! The scientist generally has several possible hypotheses in mind that relate to the question at hand. Rejecting one hypothesis eliminates one of the hypotheses and thereby brings the scientist one step closer to the truth. In fact, the scientist is usually disappointed when the hypothesis cannot be rejected because one of the possibilities has NOT been eliminated and so little progress has been made. The same group of hypotheses is still "in the running."
So, rejecting one's hypotheses does not make for a bad scientist... indeed as long as the justifiable decision is made, the scientist is performing correctly.
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