1 Chapter 1 – Scientific Method

The Scientific Method

BACKGROUND

Prior to the advent of the formal laboratory, the pursuit of science was relegated to the domain of ‘natural philosophy‘, questions pertaining to the natural world. Aristotle was the first philosopher to provide theories about observable natural phenomena. All scientists are philosophers in that they are always asking questions about the world in which they live. However, unlike the philosopher, the scientist uses a strictly defined set of rules to precisely focus this inquiry and consequent hypothesis generation onto the natural world. This set of rules pulls the pursuit of natural philosophy from a theoretical realm and redefines it, creating a separate philosophical discipline that is empirically based. In order to communicate their findings and understand the nature of life, scientists employ a process of ordered steps to generate a hypothesis, or testable statement, regarding natural phenomena. This active pursuit of natural philosophy is the realm of science, whereby a systematic method of inquiry, hypothesis testing, and experimentation is employed to provide tangible evidence toward conclusions about the natural world.

While the domain of philosophy seems boundless in its attempts to answer profound questions relating to the nature of reality, science, by definition, is bound by a series of logical steps known as the scientific method. On the journey to becoming a scientist, you must understand and practice the logic of this method so that you may employ its principles in your own pursuit of the scientific art. After all, the scientific thought process is responsible for the technological innovations we all experience.

The scientific method begins with careful observations involving characteristics (color, development, ecology, etc.) that are discrete and measurable in some fashion. Once observations have been collected, a scientist may employ one of two reasoning avenues. Deductive reasoning begins with supplied information and draws conclusions based on that specific data. For example, if all mammals have fur, and cats are mammals, then you can deduce that cats have fur. Conversely, inductive reasoning begins with specific observations and then seeks to generate general principles. For example, if you know that cats, dogs, bears, and beavers have fur and that these are all mammals, you might reason that all mammals have fur. However, when applying the scientific method, it is never okay to make such generalizations. At most, results may suggest that other experiments will follow suit, so if you know that cats, dogs, bears, and beavers have fur and that these are all mammals, then your findings can suggest that other mammals will have fur.

After observing a phenomenon, critical questions should be asked. These questions should lead, via inductive or deductive reasoning, to the generation of a testable hypothesis. The greatest hypothesis in the world, if un-testable, will always remain just that: a hypothesis. A testable hypothesis will allow the generation of a prediction, or logical consequence of a hypothesis based on the observation. It is this prediction that can be tested through experimentation.

Ideally, an experiment will remove all extraneous possibilities from the equation to allow just the hypothesis to be tested. This may involve a manipulation (e.g. changing the type of food), an addition (e.g. adding a certain chemical to affect growth), or even a deletion (e.g. removing a protein to observe its effect on development). The factor that you manipulate is known as the independent variable. The factor that you measure is known as the dependent variable. For example, suppose you wish to test the effect water temperature has on swimming speed. In this example, the independent variable would be water temperature, and the dependent variable would be swimming speed. The swimming speed will depend on the ability for one to swim at independent temperatures.

To test these factors, experimental units are divided into subsets: the control group, which is not subject to experimental manipulation, and the experimental group subjected to some condition. The control group gives you a baseline to compare your manipulations against. Without controls, it is impossible to determine whether or not an experiment actually produced the observed effect. For example, if you are using Biuret reagent to determine the presence of protein in an unknown sample, you will need to use a known sample, such as egg albumin, to verify that your Biuret reagent is properly prepared and is not contaminated. If the result desired for your known protein is verified by the results, then you can presume that your reagent was adequate and will give you appropriate results for your unknown. As you move on to more advanced science courses, you may hear more frequently that correlation does not equal causation. If you look at data, you may see that ice cream sales are positively correlated to heat strokes. A positive correlation means that two variables tend to vary together; if one increasesor decreases, the other also increases or decreases, respectively. However, this does not mean that ice cream causes heat strokes! Ice cream sales tend to increase as the outside temperature increases, and increased temperature also increases the chance of a heat stroke, so while ice cream and heat strokes are correlated, one does not cause the other.

It is also important to remember that a single observation can be misleading, therefore experiments employ multiple observations of the same manipulation. For example, if you were an alien visiting the Earth to determine the dominant life-form and happened to observe people picking up their dog’s feces, you may think that dogs are more dominant than people. With more observations of life on Earth, it becomes evident that this is not the case. Thus, sample size, or the number of observations, plays a key role in experimental set up and design. The greater the sample size, the more representative the data become, leading to greater predictive power. Sample size is often increased by conducting the same experiment multiple times. Each instance of conducting an experiment is known as a replicate.

Another factor to keep in mind is that scientists make a distinction between hypotheses and theories. The general public often uses these two terms synonymously, but they are not synonymous to scientists. A hypothesis is a testable assertion regarding some aspect of the natural world, while a theory is a broader set of generally accepted principles and hypotheses that accurately model or describe some aspect of the natural world. In short, a theory is the result of the same hypothesis tested repeatedly, garnering the same results to the point that the general scientific community agrees that the hypothesis tested is most likely true. For example, the theory of evolution is based on the collection of data from thousands of experiments that all suggest life on Earth had a common ancestor, and natural selection through mutations allowed for an advantageous adaptation for individuals with this mutation, eventually resulting in a new species. However, scientists are technically missing “missing links,” so the theory of evolution will remain a theory for the foreseeable future. Neither hypotheses nor theories are ever proven absolutely; they are always subject to change contingent on new evidence. However, if new data arises to add irrefutable evidence to a theory’s premise, a theory can become law, such as the laws of thermodynamics.

Science is more than just having a lab coat and lots of interesting equipment; it also involves answering some of life’s most difficult questions. Learning to successfully test a hypothesis by experimentation is a very important step on your way to becoming a scientist. This week you will be presented with a series of observations from which you are expected to create a testable hypothesis and execute an experiment to evaluate your hypothesis.

Primary literature, or primary sources, in scholarly journals presents the actual findings of experiments completed by scientists. These are the most important articles to consider as they are where scientists communicate the latest research and observations on a subject as unbiased data. Each magazine, textbook, newspaper, or news program is just a secondary level through which this primary information is filtered, simplified, and molded to the author’s needs or understanding of the subject. With each secondary source produced from the original, there is greater chance for misinformation, bias, and lack of detail to plague the material presented.

Three types of graphs commonly used in science are the bar graph, line graph, and pie graph. Most scientific graphs are made as line or bar graphs. Line graphs typically are used when continuous variables are being used, such as time. Bar graphs are frequently used when categorical variables, such as color, are used. There may be times when other graph types would be appropriate, but they are rare and usually used more to accentuate a point than out of necessity. The lines on scientific graphs are usually drawn either straight or curved. These smoothed lines do not have to touch all the data points, but they should at least get close to most of them. These are called best fit lines.