Practicing Scientific Processes

You might say that the work of a scientist is to solve problems. But when you decide how to dress on a particular day, you are doing problem solving, too. You may observe what the weather looks like through a window. You may go outside and see whether what you are wearing is warm or cool enough.

Scientists use an orderly approach to learn new information and to solve problems. The methods scientists may use include observing to form a hypothesis, testing a hypothesis, separating and controlling variables, and interpreting data.

Observing

You observe all the time. Scientists try to observe as much as possible about the things and events they study so they know that what they say about their observations is reliable.

Some observations describe something using only words. These observations are called qualitative observations. If you were making qualitative observations of a dog, you might use words such as furry, brown, short-haired, or short-eared.

Other observations describe how much of something there is. These are quantitative observations and use numbers, as well as words, in the description. Tools or equipment are used to measure the characteristic being described. Quantitative observations of a dog might include a mass of 45 kg, a height of 76 cm, ear length of 14 cm, and an age of 283 days.

Using Obsevations to Form a Hypothesis

Suppose you want to make a perfect score on a spelling test. Begin by thinking of several ways to accomplish this. Base these possibilities on past observations. If you put each of these possibilities into sentence form, using the words if and then, you can form a hypothesis. All of the following are hypotheses you might consider to explain how you could score 100 percent on your test:

If the test is easy, then I will get a perfect score.
If I am intelligent, then I will get a perfect score.
If I study hard, then I will get a perfect score.

Scientists make hypotheses that they can test to explain the observations they have made. Perhaps a scientist has observed that plants that receive fertilizer grow taller than plants that do not. A scientist may form a hypothesis that says: If plants are fertilized, then their growth will increase.

Designing an Experiment to Test a Hypothesis

Once you state a hypothesis, you probably want to find out whether or not it explains an event or an observation. This requires a test. A hypothesis must be something you can test. To test a hypothesis, you design and carry out an experiment. Experiments involve planning and materials. Let's figure out how to conduct an experiment to test the hypothesis stated before about the effects of fertilizer on plants.

First, you need to write out a procedure. A procedure is the plan that you follow in your experiment. A procedure tells you what materials to use and how to use them. In this experiment, your plan may involve using ten bean plants that are each 15 cm tall (to begin with) in two groups, Groups A and B. You will water the five bean plants in Group A with 200 mL of plain water and no fertilizer twice a week for three weeks. You will treat the five bean plants in Group B with 200 mL of fertilizer solution twice a week for three weeks.

You will need to measure all the plants in both groups at the beginning of the experiment and again at the end of the three-week period. These measurements will be the data that you record in a table. A sample table has been done for you. Look at the data in the table for this experiment. From the data, you can draw a conclusion and make a statement about your results. If the conclusion you draw from the data supports your hypothesis, then you can say that your hypothesis is reliable. Reliable means that you can trust your conclusion. If it did not support your hypothesis, then you would have to make new observations and state a new hypothesis none that you could also test.

Growing Bean Plants

Separating and Controlling Variables

In the experiment with the bean plants, you made everything the same except for treating one group (Group B) with fertilizer. In any experiment, it is important to keep everything the same except for the item you are testing. In the experiment, you kept the type of plants, their beginning heights, the soil, the frequency with which you watered them, and the amount of water or fertilizer all the same, or constant. By doing so, you made sure that at the end of three weeks, any change you saw was the result of whether or not the plants had been fertilized. The only thing that you changed, or varied, was the use of fertilizer. In an experiment, the one factor that you change (in this case, the fertilizer) is called the independent variable. The factor that changes (in this case, growth) as a result of the independent variable is called the dependent variable. Always make sure that there is only one independent variable. If you allow more than one, you will not know what causes the changes you observe in the dependent variable.

Many experiments also have a control, a treatment that you can compare with the results of your test groups. In this case, Group A was the control because it was not treated with fertilizer. Group B was the test group. At the end of three weeks, you were able to compare Group A with Group B and draw a conclusion.

Interpreting Data

The word interpret means "to explain the meaning of something." Information, or data, needs to mean something. Look at the problem originally being explored and find out what the data show. Perhaps you are looking at a table from an experiment designed to test the hypothesis: If plants are fertilized, then their growth will increase. Look back to the table showing the results of the bean plant experiment.

Identify the control group and the test group so you can see whether or not the variable has had an effect. In this example, Group A was the control and Group B was the test group. Now you need to check differences between the control and test groups. These differences may be qualitative or quantitative. A qualitative difference would be if the leaf colors of plants in Groups A and B were different. A quantitative difference would be the difference in numbers of centimeters of height among the plants in each group. Group B was in fact taller than Group A after three weeks.

If there are differences, the variable being tested may have had an effect. If there is no difference between the control and the test groups, the variable being tested apparently had no effect. From the data table in this experiment on page 828, it appears that fertilizer does have an effect on plant growth.

What is data?

In the experiment described on these pages, measurements were taken so that at the end of the experiment, you had something concrete to interpret. You had numbers to work with. Not every experiment that you do will give you data in the form of numbers. Sometimes, data will be in the form of a description. At the end of a chemistry experiment, you might have noted that one solution turned yellow when treated with a particular chemical, and another remained clear, like water, when treated with the same chemical. Data, therefore, are stated in different forms for different types of scientific experiments.

Are all experiments alike?

Keep in mind as you perform experiments in science that not every experiment makes use of all of the parts that have been described on these pages. For some, it may be difficult to design an experiment that will always have a control. Other experiments are complex enough that it may be hard to have only one dependent variable. Real scientists encounter many variations in the methods that they use when they perform experiments. The skills in this handbook are here for you to use and practice. In real situations, their uses will vary.