Scientific Method, Measurements and The | Research & Encyclopedia Articles

A common misconception about the scientific method is that it involves the use of exact measurements to prove hypotheses. What is the real nature of science and measurement, and how are these two processes related?

According to one definition, the scientific method is a mode of research in which a hypothesis is tested using a controlled, carefully documented, and replicable (reproducible) experiment. In fact, scientific inquiry is conducted in many different ways. Even so, the preceding definition is a reasonable summary or approximation of much scientific work.

Science, at heart, is an effort to understand and make sense of the world outside and inside ourselves. Carl Sagan, in The Demon-Haunted World: Science as a Candle in the Dark, noted that the scientific method has been a powerful tool in this effort. He concluded that the knowledge generated by this method is the most important factor in extending and bettering our lives. The steps of the scientific method follow.

1. Recognize a problem.
2. Formulate a hypothesis using existing knowledge.
3. Test the hypothesis by gathering data.
4. Revise the hypothesis (if necessary).
5. Test the new hypothesis (if necessary).
6. Draw a conclusion.

Scientists often use controlled experiments. Theoretically, controlled experiments eliminate alternative explanations by varying only one factor at a time. Does this guarantee that all alternative explanations are eliminated? To answer this question, consider the role measurement plays in the scientific method.

Measurement addresses a fundamental question that arises in many scientific studies (and everyday activities): "How big or small is it?" Answering this question can involve either direct or indirect measurements. For example, determining the size of a television screen can be done directly by using a tape measure. Many other measurements, including most of those done by scientists, must be done indirectly.

For example, if a teacher wants to know how much algebra her students learned during the semester, she cannot examine their brains directly for this knowledge. So she gives them a test, which indicates what they have stored in their brains. Likewise, astronomers cannot directly measure the distance to a faraway star, its temperature or chemical composition, or whether it has orbiting planets. They must devise ways of measuring such attributes indirectly.

Even when measurement is done directly, it is essentially an estimation rather than an exact process. One reason for this is the difference between discrete quantities and continuous quantities. To determine how much there is of a discrete quantity, one simply has to count the number of items in a collection. To determine how much there is of a continuous quantity, however, one must measure it.

Although measurement is a more complicated process than counting, it involves the following fundamentally simple idea: Divide the continuous quantity into equal-size parts (units) and count the number of units. In effect, the measurement process transforms a continuous quantity into one that is countable (a discrete quantity). For example, measuring the diagonal of a television involves, in effect, subdividing it into segments 1-inch in length and counting the number of these units.

Unlike counting a discrete quantity, measurement can involve a part of a unit. A unit of measurement can be subdivided into smaller units (such as tenths of an inch), which can be further subdivided (such as hundredths of an inch), and so on, forever. In other words, if one measures the diagonal of a 34-inch television, it is extremely unlikely to be exactly 34 inches in length. It could be, for instance, 33.9, 34.1, 33.999, or 34.0001 inches in length.

Absolutely precise measurement is impossible because measurement devices cannot exactly replicate a standard unit. For example, the inch demarcations on a yardstick are not exactly 1 inch each, even when the stick was first manufactured. Nonexact units further result from repeated expansion and contraction of the stick because of repeated heating and cooling.

Even if measuring devices were perfectly accurate, it would not be humanly possible to read them with perfect precision. In brief, even direct measurement involves some degree or margin of error.

Testing a hypothesis requires collecting data. There are basically two types of data: categorical (name) data, such as gender, and numerical (number) data. The latter can involve a discrete quantity (such as the number of men who had a particular voting preference) or a continuous one (such as the amount of math anxiety). Continuous data must be measured on a scale.

Because measurements are always imprecise to some degree, scientists' conclusions may be incorrect. So scientists work hard to develop increasingly accurate measurement devices. The possibility of measurement error is also a reason why scientific experiments need to be replicated.

Indeed, scientific conclusions based on any type of data may be imperfect for a number of reasons. One is that scientists' instruments or tests do not always measure what the scientists intend. Another problem is that scientists frequently cannot collect data on every possible case, and so only a sample of cases are measured, and a sample is not always representative of all the cases. For example, a biologist may find a result that is true for her sample of men, but the conclusions may not apply to women or even all men.

Measurement is an integral aspect of the scientific method. However, using the scientific method does not guarantee exact results and definitive proof of a hypothesis. Rather, it is a sound approach to constructing an increasingly accurate and thorough understanding of our world.

Accuracy and Precision; Data Collection and Interpretation; Problem Solving, Multiple Approaches To.

Bendick, Jeanne. Science Experiences Measuring. Danbury, CT: Franklin Watts, 1971.

Nelson, D., and Robert Reys. Measurement in School Mathematics. Reston, VA: National Council of Teachers of Mathematics, 1976.

Sagan, Carl. The Demon-Haunted World: Science as a Candle in the Dark. New York: Ballantine Books, 1997.

Steen, Lynn Arthur, ed. On the Shoulder of Giants: New Approaches to Numeracy. Washington, D.C.: National Academy Press, 1990.

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