The scientific method is a series of processes that help create questions and answer them. The scientific method is not always in the same order. Observations occur during a lot of the process and may create new questions or problems!
The scientific method often begins with observations. Observations are things that you observe or notice about the world.
Example: My dog will eat pork rinds.
Observations of nature often produce questions about how nature works. A question is sometimes called a problem statement.
Example: Will eating pork rinds make my dog gain weight?
Once a question is made, scientists make a hypothesis which is a possible explanation or answer to the question.
Example: If my dog eats pork rinds, she will gain weight.
These two terms are often confused. A hypothesis is a possible explanation or answer to a question that does not have evidence yet. A theory is an explanation or answer to a question that has a lot of evidence and has already been tested many times. An experiment needs to be done to determine if a hypothesis is accurate. Experiments have to be repeated over and over again for a hypothesis to become a theory.
After you propose a hypothesis, you perform an experiment to test it.
In good experiments, there are two groups: the experimental and control groups. Most things about these groups will stay the same. For example, if you are testing whether feeding dogs pork rinds makes them gain weight, all of the dogs should receive the same amount of exercise, live in the same type of environment, be the same age, etc. All the things that stay the same between both the experimental and control groups are called controlled variables (also known as control variables or constant variables).
The effect that you look at in an experiment is called the dependent variable. In the dog example, the dependent variable is the weight of the dogs. The weight of the dogs depends on whether you feed them pork rinds or not which is why it is called a dependent variable.
In each experiment, there is one thing that should be different: the independent variable. The independent variable is the one thing you change (or manipulate). The point of any experiment is to test the effect of the independent variable on the dependent variable. In the dog example, the independent variable is whether or not you feed the dog pork rinds. You should only have one independent variable; having more than one makes it hard to find out what the effect of the independent variable is on the dependent variable.
A good experiment produces data. Data is any statistic or fact. It is information that can help answer your question.
There are two types of data: qualitative and quantitative. “Qualitative” comes from the word “quality.” Qualitative data describes something without using numbers. For example, the dirt was dry, gray, and had cracks in it. “Quantitative” comes from the word “quantity.” Quantitative data is data that uses numbers or measurements. For example, there were 3 kilograms of dirt.
After data is collected in an experiment, you analyze (make sense of) it. Scientists often organize their data into charts and graphs and look for patterns and trends.
The graph below (which is made up) shows that the dog’s weight was measured every 7 days. On 5/3/19, the dog was 10 kg. On 5/10/19, the dog was about 11.2 kg. On 5/17/19, the dog was about 12.3 kg, and on 5/24/19, the dog was about 13.1 kg. In general, the dog’s weight trended up. The graph shows an upward trend because the line goes up to the right.
After you analyze your data, you make a conclusion. A conclusion states what the findings of the experiment were. In a conclusion, you say whether or not your hypothesis was supported by data. In our dog example, we might say that the dog gained weight each week while eating pork rinds which supports our hypothesis that if she eats pork rinds then she will gain weight.
Review:
You notice that the grass is brown. This is an example of an observation.
You ask why the grass turns brown. This is an example of a question.
You propose the possible explanation that grass turns brown when it doesn’t rain. This is an example of a hypothesis.
You grow two patches of grass that are the same species. You plant them in the same amount of soil and give them the same amount of sunlight. You give one patch of grass water, but you don’t water the other one. The grass that does not get water is the experimental group (because you’re testing whether grass turns brown when it doesn’t get water). The grass that gets water is in the control group. The species of grass, soil, and amount of sunlight are control variables. The independent variable is whether you water the grass or not. The dependent variable is whether the grass turns brown. This is how you test your hypothesis using an experiment.
You plot your data in a chart to look for patterns. This is an example of analyzing data.
You summarize your findings and state whether your data supported your hypothesis or not. This is a conclusion.