Data and Interpretation: Knowing the Difference*
Elaine Graham Kennedy
Consider the following statements:
Statement 1: A is a human being. B is a chimpanzee. A and B share many similarities, but A and B each have numerous different attributes.
Statement 2: The similarities show that both A and B descended from a common origin, i.e., a common ancestor. The differences suggest that A and B did not follow the same evolutionary path.
Statement 3: The similarities show that both A and B had a common origin - i.e., a Creator, God. The reason for the differences is that the Creator chose to make each species unique and distinctive.
The initial statement is regarded as data - observable, knowable, and open to experimentation. Statements 2 and 3 are not data - they are conclusions drawn from assumptions about the meaning of the initial statement: the first by an evolutionist, the second by a creationist.
This illustration reveals that knowledge/information can be divided into two separate concepts - data and interpretation. Since data are subject to alternative interpretations, students and researchers must carefully distinguish between the information actually included in the collected data and the "information"derived from the data, which is presented as evidence for a hypothesis. Scientists endeavor to be as objective as possible in this regard, but several factors (biases) can influence their selection and interpretation of the data.
The distinction between data and interpretation is no less important in the science classroom than in the science laboratory. Students often see interpretation parading as data - at museums, on television science shows, in nature books and magazines, and in the newspaper. Teachers have a responsibility to help students understand what is data and what is speculation.
For teachers, the greatest difficulty in separating data from interpretation occurs with the use of textbooks, a prime source of classroom information. In the science classroom, the textbooks often include more interpretation than data. Students need early training to help them distinguish between the two. This will require additional effort on the part of teachers, but it should have positive results. With practice, students will become more analytic and require fewer explanations from the teacher.
Knowing the Difference
What is data? How can anybody tell the difference between data and interpretation? Data consist of measurements and observations used as a basis for reasoning, discussion, or calculation. (1) Observable data are usually regarded as unalterable facts, although they may or may not be true. As technology and science progress, "facts" are discarded, modified, or replaced with new data. For example, measurements may form the basis for identifying an object or phenomenon. However, the identification may actually be an interpretation.
For instance, fossils of extinct organisms are often classified through measurements of various body parts that had been preserved. Despite the accuracy and precision of the measurements, correct identification is difficult because, with many of the extinct shelly fauna, scientists do not know whether large organisms whose structures are similar to those of small organisms represent different species, genders, or developmental stages. Identifications and comparisons are, therefore, not data; they are interpretations. Science, of necessity, must use identifications and comparisons as data.
Much controversy in the scientific literature is generated by a rather significant problem: interpretations drawn from limited databases. This point needs to be emphasized in every unit that is studied in any science classroom.
Complexity of Data and Interpretations
As an illustration of the complex interplay between data and interpretation, consider two steps involved in the process of identifying rocks and minerals:
Step 1. Interpreting light properties. The researchers' description of a rock's properties is based on their microscopic examination of a very thin slice of rock (commonly referred to as a "thin section"). Polarized light (light waves that vibrate in only one particular plane) is used to conduct a series of tests on each mineral in the thin section. Mineralogists use the resulting patterns to determine the mineral composition of the sample. Therefore, the identification of the minerals is an interpretation based on the light property descriptions.
Step 2. Identifying rocks. To identify rock samples, scientists examine the contact between two types of mineral and measure how much of each is present. A geologist studying the rock considers the mineral identifications "data," even though they are actually an interpretation of an interpretation. (The mineralogical "data" were derived from the light property descriptions.) Once the rock is identified, that information is used as data as well.
Just how valid is mineral or rock identification? It depends on the methods used. Conclusions can be drawn by comparing the sample with standards. For example, three thin sections may have the same mineral composition, but their mineral contacts may be very different. If the mineral grains are interlocking, the sample is said to be and igneous rock. If the mineral grains are altered, distorted, elongated, and aligned, it is a metamorphic rock. The same minerals cemented together form sedimentary rock. When terms and procedures are well-defined, identification is fairly easy and relatively reliable.
Since data are defined as only what can be measured or directly observed, teachers need to help their students learn to interpret what they read it order to arrive at reliable conclusions (and evaluate other people's conclusions!). They need to understand that an interpretation is an explanation, a means of presenting information in understandable terms. The accuracy of interpretations is limited by the availability of data and the bias of the observer.
Multiple Levels of Interpretation
Several levels of interpretations exist. For example, the name oolite not only identifies a particular rock type but also implies a history of environmental and depositional conditions involved in its formation. How can a name acquire that much interpretive information?
Level 1. The researcher finds a rock with round, beadlike particles cemented together. The particles surround a larger object in the sample, which consists of a different substance. A thin section of the sample is selected and analyzed with respect to its mineralization. The first level of interpretation is identification of the mineral composition of the little beads. The purposes of this illustration, we will say that they are particles of calcium carbonate.
Level 2. Identification of the structure of the round, bead-filled rock is based on an analysis of the relationship of a small piece of rock or shell material to the calcium carbonate that has precipitated around it. This structural information, coupled with a shape (roundness) of the particles, causes the observer to identify the beads as oolites.
At this point, one might think that the exercise is complete and that identification of the sample is as simple and straightforward as identifying the mineral and its structure. However, a third level of interpretation is introduced to explain how the oolites were formed.
Level 3. The third level relies on observations of
modern environments. Geologists know that oolites are
typically formed near a shore by the agitation of warm, shallow saline waters.
Researchers apply this knowledge to the oolitic rocks
found on a mountainside. In other words, geologists use what they know about
the modern setting to interpret to the earlier environment. They assume that
the ancient oolites on the mountain formed there in
the same way that modern oolites form in the ocean or
The exercise is not over. This set of interpretations is now added to other data (which also have multiple interpretations) to arrive at a final description of a particular rock exposure. The process is duplicated at other exposures or outcrops of rock over a broader region in developing a model.
Level 4. Geologists use other rock types and additional data to develop models of geologic events in Earth's history. For example, cemented quartz grains are called sandstones. Patterns in sandstone may occur as the result of a process known as cross-bedding. Typically, cross-beds are formed as currents (wind and/or water) deposit sand and silt on the slope of dunes sheltered from the wind. By integrating a broad range of data and interpretations (the minerals, rocks, oolites, and cross-bedding), geologists develope a fifth level of interpretation: modeling. Models provide scientists with a generalized framework for developing predictions and drawing conclusions about events that may have occurred in the past. (2)
Why is it necessary to clearly distinguished between data and interpretation when evaluating research? Data are actual measurements and observations. Interpretations are an attempt to identify or explain what is measured and observed. An interpretation's validity depends on how well it accommodates the available data. Interpretations may change as the database changes. This interplay between data and interpretation is what makes science so successful and progressive.
Bias During Data Acquisition
Scientists realize that they are prone to errors and misconceptions. Hence, they try to maintain an attitude of objectivity in research. (3) This commitment to objectivity has created an aura of infallibility around science and scientists. Reports in the popular press and on television oversimplify the work of scientists, implying that when a scientist draws a conclusion, all competing theories have been repeated and all questions have been resolved. This encourages a false sense of security and confidence in science. Some scientists do little to dispel this image.
To complicate matters, the scientific community has adopted the position that any researcher having a religious bias is nonscientific; therefore by definition, creation science cannot be true science. Such an attitude fails to recognize its own biases. (4)
Here are some biases that influence science - some technical, others subtle and unconscious factors:
1. Sampling constraints. The first problem in gathering data is sampling bias. Every scientist has some preconceived ideas that influence his or her selection of data. Random sampling helps minimize these problems, (5) but even so, sampling choices often favor a particular hypothesis.
2. Systematic errors. A scientist may have a "blind spot," a failure to recognize data. For example, it is common for a paleontologist who specializes in fossil snails to collect a wider variety of gastropods than other people searching a mountainside. However, that same individual will discover fewer clams and corals than other fossil collectors do at the site. An accurate tally of the types of fossils can have a significant impact on the interpretation of that site, but the bias of the researcher may affect the accuracy of the count.
The processing of data can also introduce systematic technical bias. (6) An unrecognized faulty procedure or an incorrectly applied mathematical or statistical formula during data processing introduces a systematic error or bias into the results.
3. Technological constraints. Scientists can incorporate large quantities of data and interpretations into computer-generated models to do analyses involving pattern recognition. However, the use of gigantic databases does not necessarily ensure that models adequately reflect complex systems and processes. The models created by computer -generated software produce technological biases because their simplified parameters limit the model's applications to real systems. (7)
4. Quality of data. Data analysis introduces bias because qualitative or subjective interpretations are embedded in the conclusions. For example: In using potassium-argon dating techniques, the quantity of these elements in a given sample can be measured very accurately and precisely. However, it is difficult to know just what the data mean. Conclusions about the age of the sample depend heavily on numerous assumptions and are affected by problems in the methodology, including human error. (8) Current technology cannot measure the age of the fossiliferous sedimentary rock directly; thus, the conclusions are biased because they are based on an analysis of associated igneous material, which may or may not provide valid ages. Descriptive data (information that is not quantifiable) are even more problematic.
5. Financial constraints. The scientific method requires rigorous testing before theories can be accepted. However, time and monetary constraints limit the crucial testing process. New data are incorporated into current theory because it is easier to get material published if one's conclusions are widely accepted by the scientific community. The funding process has an incredible influence on research today. (9) No papers published, no money for research. It's that simple.
The rigorous testing demanded by the scientific method is not cost effective, so ideas and concepts are frequently rushed into print and then cited in subsequent publications. Monetary pressures increase the technical bias by limiting the experimental process. Students should be aware that research funding exerts significant controlled over what gets into print.
Implications for Science and Religion
When it comes to the interface between science and religion, several points need to be noted:
First, not all data are accurately measured, and it is sometimes difficult to differentiate between data and interpretation. Multiple interpretations of any database are not only possible but also plausible. Although data interpretation can be very complex, the simplest scenario is usually preferred to more complex ones in present theory development.
Second, bias is present in every interpretation because all scientific interpretations are at least partly subjective.
Third, the public needs to understand how science works. People sometimes get alarmed because scientific interpretations are changing constantly, making it hard for them to know what to believe. However, that is the nature of science - that is how it advances.
Fourth, although science provides relevant information about the world around and scientific discoveries how often been a blessing to humanity, Christians should not base their theological beliefs on scientific concepts. If science is allowed to dictate theology, then every time scientific interpretations change, theology will have to change, whether or not the alterations are consistent with one's belief system and experiences.
At the same time, theology must not dictate how scientists do their work or the conclusions they form. Concepts such as "fixity of species," based on theology taught by church leaders in the 17th and 18th centuries, (10) and geocentric theory are some of the ideas that contributed to conflict between science and theology.
The Bible can provide both legitimate working hypotheses and constraints for science. In fact, Scripture often suggests avenues for investigation that would not be considered by most non-Christians. Such research should knowledge any scriptural bias that may be present. As in all good science, the data must be carefully evaluated.
Scientists are fully aware of many of these issues. However, especially in the area of origins, science alone cannot possess the complete database because the scientific approach does not consider the possibility of supernatural involvement in nature or in Earth history. Furthermore, neither creation nor evolution scientists observed the events that occurred long ago in the universe, nor can they replicate them. Both processes are needed to order to draw reliable scientific conclusions from the data.
Most scientists believe there are it reconcilable conflicts between science and Scripture. (11) For example, Ayala states, "To claim that the statement of Genesis are scientific truths is to deny all the evidence. To teach such statements in the schools as if they were science would do untold harm." (12) In fact, scientific evidence does not prove either a long or short history for life. The available evidence provides very limited information.
Data are not the primary problem in reconciling science and Scripture. It is the interpretation of the data that creates conflicts. It has been said: "Not only is the present the key to the past, but the present is a key to the future." (13) Both the Biblical accounts of a worldwide flood and the prophetic accounts of Christ's second advent proclaim the falsity of that concept. (14)
For Christians, the Bible provides a philosophical basis for belief as well as a source of information that suggests an additional way to approach the study of nature. Using this perspective, some harmony between science in Scripture may be achieved. In fact, Christians maybe the ones most likely to find harmony because they recognize God as the Creator of nature and its scientific laws.
*Article is reproduced by permission of the Journal of Adventist Education. Publication may be found in JAE Feb/Mar 2003.
Helping Students Separate Data From Interpretation
This exercise can work in various disciplines. Science teachers may want to use a chapter from a science textbook or an article from a newspaper or a popular science magazine. Use several different sources and use at least one that presents conclusions without any data. History teachers might find several paragraphs from a history textbook or articles from newspapers or journals useful for creating a similar analytical exercise in their own discipline. Teachers in other disciplines such as Bible, English and health may want to try this exercise by using a textbook or magazine article with their students as well.
Before class, prepare a paragraph or section from the selected source by making copies to share with students, or write the information on an overhead or board. Instruct the students to highlight or underline those items in the paragraph that can be measured or observed directly. Then, ask the class to highlight or underline (in a different color) conclusions, speculations and other statements that tell the reader what to believe. Ask the students their conclusions regarding the available information. Teachers may prefer to have the students work in small groups so that the assessment phase represents the ideas of the group; however, the point of the exercise is to help each individual student learn to assess the information and draw personal conclusions regarding the validity of the author's argument. For additional assistance on this project, teachers may contact the author by E-mail at firstname.lastname@example.org or consult the Geoscience Research Institute at http://www.grisda.org/
1. Webster's College Dictionary (New York: Random House, 1991).
2. Andrew D. Miall, Principles of Sedimentary Basin Analysis (New York: Springer-Verlag, 1984), p. 5.
3. Francisco Ayala, Robert McCormick Adams, Mary-Dell Chilton, Gerald Holton, Kumar Patel, Frank Press, Michael Ruse, and Phillip Sharp, On Being a Scientist (Washington D.C.: National Academy of Sciences Press, 1989), p. 1.
4. Del Ratzsch, The
5. Ayala, et al., p. 5.
6. Ibid., pp. 5, 6.
7. Ibid., p. 6.
8. C.M.R. Fowler, The Solid Earth: An Introduction to Global Geophysics (Cambridge: Cambridge University Press, 1998), p. 192.
9. Francisco J. Ayala and Bert Black, "Science and the Courts," American Scientist 81: 3 (May/June 1993), pp. 230-239.
10. E. Janet Browne, The
11. Colin Norman, "Nobelists Unite Against 'Creation Science,'" Science 233: 4767 (Aug. 29, 1986), p. 935.
13. Alan Baharlou, 1978. Personal communication that echoes the sentiment of James Hutton in 1788, "The results, therefore, of our present inquiry is that we find no vintage of the beginning - no prospect of an end" (from Transactions of the Royal Society of Edinburgh).
14. II Peter 3: 3-10.