THE SCIENTIFIC METHOD
Objectives: By the end of this exercise, you should be able to
1. Describe the 4 steps of the scientific method.
2. Know the types of variables, and the importance of replicates and control groups.
3. Analyze and graph scientific data.
4. Compute a mean and standard deviation.
Observation: Gathering of Facts
As any process, or sets of things are observed, some facts emerge about them. They might have a certain shape, size or relationship to other things. One gathers these facts all the time, often without consciously being aware you are doing it. Occasionally, our observance of these facts leads us to wonder why something has come to have these characteristics. We may hazard a guess. If this guess can be put to a test, then we have just formulated a scientific hypothesis. Step 1 of the scientific method is to identify a question or problem you wish to investigate.
Hypothesis: A Statement of Relationship
A hypothesis consists of a testable guess as to how or why something occurs the way it does. If repeated testing of the hypothesis does not turn up any new facts that negate the hypothesis then the hypothesis may be elevated to a theory. Note that in scientific terms, the word theory denotes a principle or generalization that has been tested over and over, and has withstood the test of time. A theory can also be considered as a cluster of hypotheses with explanatory value, well supported by the data. In contrast, the general public uses the word theory to imply a lack of knowledge, or a guess. Darwin's Theory of Evolution by Natural Selection is an example of a theory that is basic to biology, just as Einstein's Theory of General Relativity is accepted and basic to the study of physics.
An hypothesis is usually formally drafted as a statement of relationship, i.e. the
factor being observed is dependent /or not dependent on the factor being tested. Some examples are - "The lung cancer rate in a population is dependent on the amount of exposure to air pollution.” "The rate of photosynthesis in a plant is dependent on the intensity of light received." Once you have a formal hypothesis, you can make predictions about what will happen if the hypothesis is true. Step 2 of the scientific method is to formulate a hypothesis and make predictions based on it.
Testing: Designing an Experiment
Once formulated, a hypothesis may be tested under rigorous, repeatable conditions. An experiment is a test of a particular hypothesis. Such experiments must be designed in such a way as to be repeatable by all who attempt them. Often, testing may involve sophisticated equipment, but in this context the equipment is only a tool to help us answer the question that has been posed. An experiment usually is designed to test just one of the many variables or factors that could affect the outcome or results. (It is possible to test more than one variable at a time, but this requires a more complex design and analysis.) The characteristic being tested is called the independent variable. The parameter being measured (the result) is called the dependent variable. All the other factors or variables that might effect the outcome must be controlled or accounted for, therefore these are called the controlled variables. These factors are not supposed to vary, they remain the same or constant throughout the experiment. Most experiments are done many times, or observations are made on many subjects: these are referred to as replicates. Replicates help eliminate some of the differences in the results that may be due to individual variability in the test subjects. Sometimes the test subjects are divided into two groups; one group experiences variations in the independent variable, therefore it is called the experimental group. The other group is not exposed to changes or deviations from its normal environment, therefore it is called the control group.
A literature search precedes the actual experimental design, and colleagues in the field of study are consulted. Usually, pilot studies are done to determine if the experimental design is adequate and practical. Often, one has to "go back to the drawing board" and revise the design. Designing and conducting repeated experiments to test your hypothesis is Step 3 of the scientific method.
Evaluation: Data Analysis
After the hypothesis has been clearly stated, and experiments performed to test its validity, the data must be gathered in such a way that the validity of the hypothesis can be evaluated. Usually such data is compiled in Tables or Figures (graphs). Raw data is almost never presented in a scientific paper. Thus learning how to present data clearly in Tables or Figures is an important skill.
Step 4 of the scientific method is the analysis of data and evaluation of the hypothesis given the experimental results. After evaluating the acquired data, it is time to accept or reject or perhaps modify the hypothesis. Should a modification of the hypothesis seem appropriate, then this whole process can be repeated. Before a scientific paper is published, it is usually subjected to a review by several experts in the field of study. After publication, it is again subject to the critical evaluation of peer scientists. It is through this process of repeated testing, evaluating and modifying that scientific theories are developed. It is because of these processes that science is essentially a self-correcting enterprise.
STATISTICAL ANALYSES OF DATA
The collection, statistical analysis and interpretation of data are essential parts of most scientific research. Rows and columns of numbers, such as those generated by the our class experiments, rarely provide much useful information without further manipulations, and it is nearly impossible to compare data from several experiments by visual examination alone. Imagine trying to interpret the results of thousands of such experiments without some means of statistical comparison. Although there are perhaps hundreds of different methods of statistical analyses, some of which require complex calculations and computers, a few are relatively easy to calculate and they yield valuable information. The range of an array of data represents the spread between the lowest and highest value. The mode is the most frequent class or value. The median is the middle value of an array of data, therefore half the values are greater than it and half the values are less than the it. The mean or arithmetic average (`C) is a much more useful value than either the mode or median. It is the sum (S) of the observed values (x) divided by the number of observations (n).
However, means give no indication of variation within a sample. Two different sets of data may have identical means if all the data are clustered very closely to the mean or if the data are markedly above and below the mean. For example, students A and B may have identical mean exam scores of 75 in Biology class but one may have individual scores of 74, 75 and 76 while the other has scores of 50, 75 and 100. The mean alone does not reflect the relative consistency or variation in their exam performances throughout the term. The variance (S2) provides an estimate of the degree of dispersion or variation within a particular sample. Variance is calculated by summing the squares of the deviations of all the observations from their mean and dividing by n-1.
S2
= 
The standard deviation (S) is a more commonly used estimate of the degree of variation within an array of values. It is merely the square root of the variance:
S =
Standard deviations, when used in conjunction with means, allow different sets of data to be compared in meaningful ways.
GRAPHING QUANTITATIVE DATA
Most people find it easier to comprehend the significance of data expressed visually rather than numerically. When expressing scientific data one picture is indeed often "worth a thousand words". Scientists often use graphs to visually present data. Graphs consist of perpendicular axes, with the horizontal called the x axis (abscissa) and the vertical called the y axis (ordinate). Graphs sometimes reveal relationships between two variables, for example, one variable (the dependent variable) may be affected by the other (the independent variable). The independent variable is plotted on the x axis and the dependent variable is plotted on the y axis. If two characteristics appear to be related, they are said to be correlated. For example, there is a strong correlation between smoking and lung cancer. Correlations allow scientists to make predictions when only one of the variables is known. For example, the more a person smokes, the more likely they are to develop lung cancer.
Scatter diagrams are constructed by plotting individual points on x, y axes. As the name implies, line graphs are constructed by plotting individual values on x, y axes and connecting them via lines. Histograms, also called frequency diagrams, are constructed by grouping data into discreet classes, represented by blocks, and plotting them versus their frequency of occurrence on the x and y axes, respectively. For example, numerical grades are usually grouped into the classes A, B, C, D and F. Graphs often reveal that although there may be considerable variation in the data (a wide range), most of the values occur relatively close to the mean. This is known as central tendency.
Graphs of scientific data, particularly biological data, frequently yield normal (bell-shaped) curves or distributions. Such normal distributions have the following characteristics: the mean, median and mode are identical; 68% of the total variation falls within the range of plus or minus one standard deviation; and 95% of the total variation falls within the range of plus or minus two standard deviations. Recall that standard deviation is a measure of the variation or dispersion of the data about the mean. Visually represented on a line graph, data with a small standard deviation forms a sharp, narrow peak like a mountain, whereas a large standard deviation yields a graph shaped more like a broad hill or dome.
SCIENTIFIC WRITING
(from Brower, Zar & Ende, 1989)
Just as there are common methods to scientific research, scientific writing has a fairly uniform style which reflects the investigative process. At a minimum, scientific reports contain at least four sections: Introduction, Material and Methods, Results and Discussion.
1. Introduction Section
In the introduction of the paper state the nature of the problem, objectives of the study and any hypotheses to be tested. Also, give a brief background for the study, which would typically include a brief review of the literature. Relate the problem and its significance to the general discipline of study. This part of the paper presents the background, justification, and relevance of your study.
2. Procedure
Procedures in research reports are usually detailed enough for the reader to have an accurate idea of what was done in the study or to be guided to appropriate literature for this information. A good description of materials and methods used is one that would enable a reader to duplicate your investigative procedure. Keep to a minimum the details of standard and generally known procedures (such as how an item was weighed). In a field study, a general description of the study site is called for. If this description needs to be lengthy, then it may comprise a separate subsection or a new section. Materials used in the investigation are not merely listed, as in a cake recipe, they are woven together with the methods in a narrative form.
3. Results Section
This portion of a report gives the facts found, even if they are contrary to the hypothesis or expectation. Listings of raw data are rarely presented, except occasionally in a class activity or as an appendix to the report. Instead, data typically are summarized using means, frequency tables, percentages, or other descriptive statistics for presentation and analysis in some appropriate statistical manner. These data summaries may be incorporated into figures or tables if this results in additional clarity or helps illustrate a pattern or trend. In general, the number of data collected (sample size, n) should be indicated, and some measure of variability of the data should accompany statements of means. Statistics used, type of data analysis performed, and mode of presentation depend on the study and type of data collected. Statistical comparisons of different groups of data are often called for. The results section is not just a data summarization or a collection of tables and figures; it should contain an explanation and description of the data. Tell the reader exactly what you found, and what patterns, trends, or relationships were observed. For example, do not just say "The species-area curve is shown in figure 1." Tell the reader what is being presented, "Figure 1 shows that the number of species in the habitat increases and then levels off as the area of the habitat increases." Illustrations in the results section may consist of graphs, photographs, or diagrams that visually depict your results. All such illustrations are individually numbered, cited in the text and referred to as a figure. Labeling and citing tables of data in the text is done in the same manner as for graphs. If a graph will summarize the data as well or better than a table, then the graphical presentation typically is preferable. Each figure and table should contain an explanatory legend. Be sure the axes of all graphs are fully and correctly labeled with a scale marked off and the units of measurements given; units of measurement (metric) must also be given for tabular data. Avoid the tendency to cram too much information into one graph or table, thus losing readability.
4. Discussion Section
In the previous section of the paper the results are summarized and described. In this section they should be interpreted, critically evaluated, and compared to other research reports; and conclusions should then be drawn based on the study and its findings. Whereas the results section presents the "news" the discussion section contains the "editorial."
In the discussion, examine the amount and possible sources of variability in your data. Examine your results for bias and evaluate its consequences in data interpretation. Develop arguments for and against your hypotheses and interpretations. Do not make generalized statements that are not based on your data, known facts, or reason. Be sure to relate your findings to other studies and cite those studies. Draw positive conclusions from your study whenever possible.
OUR EXPERIMENT
Activity 1:
To illustrate the use of the scientific method, we will conduct a simple experiment involving the growth of some plants. We will assume that the only facts you know about our plants are the following:
1. Plants use light to make their own food through photosynthesis;
2. Seeds store a certain amount of food to support the early growth of seedlings.
The question we wish to answer is:
Do plants grown from seeds in the light grow taller than plants grown from seeds in the dark?
Stated as an hypothesis, this becomes:
Plant growth, as measured by stem length, will be greater for plants grown from seed in the light than in the dark (Stem length is dependent on the amount of light.)
Your task is to design an experiment to test this hypothesis, plant the seeds, and then make measurements of the stem length of the plants over the next two weeks. After that period of time, our class data will be summarized in Tables and Figures so you can evaluate this hypothesis. You will write a short scientific paper based on the guidelines above. Due date:
Activity 2:
In this activity, we will use the scientific method to investigate the relationship, if any, between temperature and seed germination. The question we wish to answer is: is seed germination affected by temperature? Our hypothesis might be: Seed germination is not dependent on temperature.
Each pair of students will prepare three petri plates, one to be incubated at each of three temperatures, chosen to represent cool (~ 8°C or 46°F), moderate (~ 18°C or 64°F) and warm (~28°C or 82°F) environments. Count out 30 seeds of your assigned species, and place in a small beaker. Cover with 5% bleach for 10 minutes to kill fungal spores. Disinfect your forceps at the same time. Make sure all the seeds are submerged. While the seeds are soaking, obtain 3 petri plates, and label with temperature, seed type, and your initials. Place 2 layers of filter paper or paper towel cut to proper size in the bottom of each petri plate. Dampen with tap water until paper is thoroughly moist and glistens. Pour off excess water if necessary. With forceps, remove seeds from bleach and place 10 soaked seeds on each plate. Spread out as evenly as possible. Cover plates and place in the tray assigned for each temperature. You will count the # of germinated seeds on each plate next week.
After we collect the data, our class data will be summarized in Tables and Figures so you can evaluate this hypothesis. You will write a short scientific paper according to the guidelines above. Due date:
Questions for Activities 1 and 2: In our experiment:
What is the dependent variable?
What is the independent variable?
What are some of the controlled variables?
Is there a control group and an experimental group?
What are the possible sources of error?
How would you graph or summarize the data?
General Questions:
1. What is the difference between a theory and a hypothesis?
2. What are replicates and why are they used?
3. How is peer review important in science?
4. What are the 4 steps of the scientific method?
5. What information does the standard deviation convey that is not obvious from the mean?