Forming Operational Definitions  Operational definitions define an object by showing how it functions, works, or behaves. Such definitions are written in terms of how an object works or how it can be used; that is, what its job or purpose is. Some operational definitions explain how an object, such as the car in Figure 15, can be used.

• A car is a vehicle that can move things from one place to another. Or such a definition may explain how an object works.
• A car is a vehicle that can move from place to place.

Forming a Hypothesis

Hypotheses  A hypothesis is a prediction, based on observation, that can be tested. Hypotheses are often stated as if-and-then statements. A hypothesis needs to be testable.

For example, a scientist has observed in Figure 16 that plants that are fertilized grow taller than plants that are not. A scientist may form a hypothesis that says: If plants are fertilized, then they will grow taller. This hypothesis can be tested by an experiment.

Designing an Experiment to Test a Hypothesis

Possible procedural steps.  Once you have stated a hypothesis, you probably want to find out whether or not it explains an event or observation. In order to test a hypothesis, you must perform an experiment. When conducting an experiment, it is best to begin by writing 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. After following the procedure, data are obtained. From this data, you can then 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-just make sure that it is one that you can test.

Planning a Procedure  Suppose you hypothesize that a houseplant will grow better if watered with bottled water than with tap water.

Let's figure out how to conduct an experiment to test the hypothesis: If a plant is watered with bottled water, then it will grow better and look healthier. An example procedure is seen in Figure 17. The data generated from this procedure are shown in Figure 18. The data show that the plant grew the same amount each month regardless of the type of water used. This conclusion does not support the original hypothesis made.

Separating and Controlling Variables
In any experiment, it is important to keep everything the same except for the item you are testing. The one factor that you change is called the independent variable. The independent variable in the experiment on the previous page was the type of water. The factor that changes as a result of the independent variable is called the dependent variable. The dependent variable in the experiment was the plant height. Always make sure that there is only one independent variable. If you have more than one, you will not know what caused the changes you observe in the independent variable. Many experiments have a control. A control is a treatment or an experiment that you can compare with the results of your test groups.

In the experiment with the plants, you made everything the same except the type of water being used. The soil, the amount of water given, the amount of light, and the temperature of the room should remain the same throughout the entire experiment. By doing so, you made sure that at the end of the experiment, any differences were the result of the type of water being used — bottled or tap. The type of water was the independent factor, and the height of the plant was the dependent factor.

Interpreting Data
The word interpret means "to explain the meaning of something." Look at the problem being explored in the plant experiment. and find out what the data show. When you looked at the data you collected, you were checking to see if the variable had an effect. You were looking for an explanation. If there are differences in the data, 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 has had no effect.

Look back at Figure 18, which shows the results of this experiment. In this example, the use of tap water to water the plant was the control, while watering the plant with bottled water was the test. Data showed no difference in the amount of plant growth over a one-month period.

What are data?  In the experiment described on these pages, measurements were taken so that at the end of the experiment, you had concrete numbers to interpret. 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 and another remained treated with a particular chemical, clear 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 situations, 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. Scientists use many variations in their methods of performing experiments. The skills in this handbook are here for you to use and practice. In real situations, their uses will vary.