The Scientific Method
The scientific method is a technique for testing ideas with observations. There is nothing mysterious or intimidating about the scientific method; it is merely a formalized version of the way any of us might naturally use logic to resolve a question.
Instructions: Follow the tabs to improve your vocabulary development and understanding of the steps in the Scientific Method.
Now read part 1 and notice how those pre-reading vocabulary items (in bold) are used in context.
Develop a hypothesis
Scientists address their questions by devising explanations that they can test. A hypothesis is a statement that attempts to explain a phenomenon or answer a scientific question. For example, a scientist investigating the question of why algae are growing excessively in local ponds might observe that chemical fertilizers are being applied on farm fields nearby. The scientist might then propose a hypothesis as follows: “Agricultural fertilizers running into ponds cause the amount of algae in the ponds to increase.”
The scientist next uses the hypothesis to generate predictions, specific statements that can be directly and unequivocally tested. In our algae example, a researcher might predict: “If agricultural fertilizers are added to a pond, the quantity of algae in the pond will increase.”
Test the predictions
As you read Part 3 try to understand or guess the meaning of the bold terms and phrases, then go on to the next tab to check your understanding
Analyze and interpret results
Scientists record data, or information, from their studies. They particularly value quantitative data (information expressed using numbers), because numbers provide precision and are easy to compare. The scientist running the fertilization experiment, for instance, might quantify the area of water surface covered by algae in each pond or might measure the dry weight of algae in a certain volume of water taken from each. It is vital, however, to collect data that is representative. Because it is impractical to measure a pond’s total algal growth, our researcher would instead sample from multiple areas of the pond. These areas must be selected in a random manner, since choosing areas with the most growth or the least growth, or areas most convenient to sample, would not provide a representative sample.
Even with the precision that numbers provide, a scientist’s results may not be clear-cut. Data from treatments and controls may vary only slightly, or replicates may yield different results. The researcher must therefore analyze the data using statistical tests. With these mathematical methods, scientists can determine objectively and precisely the strength and reliability of patterns they find.
Some research, especially in the social sciences, involves data that is qualitative, or not expressible in terms of numbers. Research involving historical texts, personal interviews, surveys, case studies, or descriptive observation of behavior can include qualitative data on which quantitative statistical analysis may not be possible. If experiments disprove a hypothesis, the scientist will reject it and may formulate a new hypothesis to replace it.
If experiments fail to disprove a hypothesis, this lends support to the hypothesis but does not prove it is correct. The scientist may choose to generate new predictions to test the hypothesis in different ways and further assess its likelihood of being true. Thus, the scientific method loops back on itself, often giving rise to repeated rounds of hypothesis revision and new experimentation.
If repeated tests fail to reject a hypothesis, evidence in favor of it accumulates, and the researcher may eventually conclude that the hypothesis is well supported. Ideally, the scientist would want to test all possible explanations. For instance, our researcher might formulate an additional hypothesis, proposing that algae increase in fertilized ponds because chemical fertilizers diminish the numbers of fish or invertebrate animals that eat algae. It is possible, of course, that both hypotheses could be correct and that each may explain some portion of the initial observation that local ponds were experiencing algal blooms.
Withgott, J., & Laposata, M. (2015) Environment: The science behind the stories, global edition. Pearson