Physical Science 8th grade (1).pdf

Analysis the process of evaluating data analysis may

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analysis - the process of evaluating data. Analysis may include thinking, creating graphs, doing calculations, and discussing ideas with others. conclusion - a statement of what was learned in an experiment or observation. Designing a good experiment The importance of experiment design A good experiment is carefully planned and carried out. If the experiment is well designed, the results will help you learn what you want to know. If the experiment is poorly designed, the results may fool you into believing the wrong things. Many people have been mistaken and confused by results of poorly designed experiments. The hypothesis The first step in designing a good experiment is to clearly state what you want to test (the hypothesis ). Remember, a hypothesis is a tentative statement about how or why something happens. For example, “Cars rolling down steep ramps go faster than those rolling down shallow ramps” is a good hypothesis. It is good because it may be tested by doing an experiment (Figure 2.15). The procedure The second step is to plan how you will test your hypothesis. This part of experiment design is called the procedure . A good procedure describes the equipment you use, the techniques you use, and the data you collect from the experiment. For example, you could measure the time it takes a car to roll between two photogates as you vary the angle of the ramp. The procedure should contain enough information for someone else to repeat your experiment the exact same way you did. Data The purpose of experiments is to collect scientific evidence. In many experiments the scientific evidence will be data . Data are usually the values of variables that you measure during an experiment. For example, the ramp angle and time between photogates are data. Analysis and conclusion Once you collect your data, you need to analyze it. Analyzing includes thinking about, graphing, or doing calculations with the data. If your experiment was successful, your analysis will lead you to a conclusion . A conclusion is a statement of what you learned from the experiment. A conclusion must always be supported by data and analysis. For example, that cars roll faster down steeper ramps is a conclusion supported by data that shows what the cars did on ramps of various heights in the experiment.
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39 2.2 E XPERIMENTS AND D ATA C HAPTER 2: E XPERIMENTS AND V ARIABLES Experimental and control variables Only one variable is changed in an ideal experiment In an ideal experiment only a single variable is changed at a time. If anything happens in the experiment, you can then assume what happened was caused by the variable you changed. On the other hand, if you make many changes at once, you will never be able to sort out which one caused what happened. The experiment will still work , you just won’t be able to understand its results.
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