Lab 1: Introduction To The Scientific Method

LabLab #1: #1: AnIntroduction introduction to to the the scientific Scientific method Method

Objectives: 1. To explain how scientists seek to improve our understanding of natural phenomena. 2. To put the scientific method into practice.

Observations

I. Introduction

One of the first questions a child asks an adult is “why?” and this is arguably one of the central characteristics of human behavior – we have an abiding curiosity about the world around us. Indeed, our knowledge and understanding of the world around us has progressed increasingly rapidly over the last 200 years. How is it that so much has been learned in relatively short period of time?

Scientists have formalized a process that enables them to solve problems (gain knowledge) in a consistent and repeatable fashion. Because science is inherently a public activity, the consistency and repeatability are two key aspects, allowing others (scientists and non scientists alike) to evaluate independently the results achieved. All good science has a similar structure, referred to as the scientific method, although scientists and textbooks will vary in the actual words used. In lab, we are going to use the language described in the figure below:

II. Using the Scientific Method: An Example

For this lab, we are going to use the scientific method to explore a topic that has always attracted attention: are there differences between men and women that lead them to make different career choices? Generalizations about such behavioral differences are a constant underlying (although politically incorrect) assumption of human society, the basis of many jokes and popular books (i.e., “Taking Sex Differences Seriously” by Rhoads, 2004).

In the spring of this year, the possibility that academics might take this politically incorrect position seriously was raised by a speech given by the president of Harvard University, Lawrence Summers (a full transcript of the speech is available at http://www.president.harvard.edu/speeches/2005/nber.html and his apology http://www.president.harvard.edu/speeches/2005/womensci.html). As summarized in Science Magazine (28 January 2005, Volume 207 pg 492-492), “On 14 January the president of Harvard University, Lawrence Summers, triggered a national uproar when he said at an academic conference that genes and personal choices may help explain why so few women are leaders in science and engineering fields”. The response by scientists across the US was fairly uniform and rapid. As stated by a letter published in the 18 February issue of Science, written by a large group of men and women, “…we are concerned by the suggestion that the status quo for women in science and engineering may be natural, inevitable, and unrelated to social factors”.

However, it is undeniable that there are physiological differences between males and females in all bisexual organisms, and it is undeniable that there are many fewer women in the physical sciences and engineering. Is there any possibility that physiological differences may lead to different approaches to problem solving that could have a negative impact upon women’s success in science and engineering?

To approach this scientifically, our question must be restated as a testable hypothesis: an hypothesis for which we can design an experiment that could result in data that cause us to reject the hypothesis as false. Note that we are phrasing this in terms of rejection not proof of our hypothesis. Science relies upon rejection of reasonable alternative hypotheses, because we can never prove that an hypothesis is true: there is always the chance that we have failed to imagine and test a better model of how something works.

Once we have stated the question as a testable hypothesis, we can establish the results we expect if it is correct, or predictions. We will then do an experiment to collect data that might result in rejection of our hypothesis: such data would be contrary to our predictions. Comparison of the data we collect with the predicted outcome forms the step of evaluation. Finally, we can use the process of evaluation to revisit the initial observations, perhaps formulating new questions or hypotheses, a step scientists refer to as drawing inferences.

We can set this process up in the following table. During discussion in lab, we ask you to fill in the blank cells in the table.

OBSERVATION The act of making or There are more men in recording pattern. the physical sciences than women. QUESTION To ask why. Why are there more men in physical sciences? HYPOTHESIS A testable mechanism or explanation for the observation.

EXPERIMENT/ A manipulation that PREDICTIONS could produce data that would cause us to reject the hypothesis.

Predicted results if our model is correct. EVALUATE/ Compare the data to DRAW INFERENCES our predictions. Draw inferences about this and other possible mechanisms.

III. COLLECTING DATA TO TEST OUR HYPOTHESIS

There are many standard tests of intelligence. One type that is specifically designed to evaluate alternative problem-solving pathways is the Multiple Intelligence test. These tests have developed out of a theory of cognition initially developed by Howard Gardner in the 1980s. He stated that people have different cognitive strengths and different cognitive styles, some innate (genetic) and somewhat cultural (learned, or environmental). He described eight different intelligences, or problem-solving types: linguistic, logical (mathematical), musical, spatial, bodily, interpersonal, and intrapersonal. More can be learned about these different types of intelligence at http://www.mitest.com/omultint.htm. The MI tests are developed to determine which of these intelligence types a particular person has (for instance, people with ADD or ADHD often have particular intelligence types, and these MI tests are used in their evaluation).

We have copied out a standard multiple intelligence (MI) test, and will use this to test whether there are intrinsic differences between men and women by asking each person in the lab group to take the test, then comparing the mean scores of men and women.

Your TA will distribute the test. Please keep score on a separate piece of paper, as you will later use the test for homework. Turn your scores for each of the sections into the TA, and the TA will calculate totals and averages for men and women in each section. IV. EVALUATION OF THE DATA

Examine the means and totals for men and women in your lab group for each section of the MI test. Try to answer the following questions:

We won’t do any statistical tests at this time, but do there appear to be any differences between the sexes?

Do these data agree or disagree with your predictions?

Do these results cause you to reject your hypothesis?

There are likely many issues with these data; they are unlikely to be a particularly good data set in that they are unlikely to be a good representation of the “population as a whole”. Consider how the following things might influence the results:

What is the sex ratio (men/women) in your lab section? How might a deviation from 50:50 influence the results?

Who is taking your class? In other words, is the lab group a random selection from the “population as a whole” or even the entire UVM student body, or is there some degree of self-selection?

What is the total sample size in the class? If too few individuals are included in an experiment, there is some possibility that simply by chance the individuals tested won’t be representatives of the “average” or “norm” of the population as a whole: they will be outliers.

There are two other points bearing on these results and the inferences we can draw from them that we would like you to consider: a. Does this test separate (distinguish) innate and cultural differences? b. Is it obvious that differences, if they exist, in MI scores between men and women might actually be causing differences in success in the physical sciences and engineering?

V. The Cyclic Nature of Science So what happens if your results lead you to reject your hypothesis? Then you need to go back and examine the original hypothesis. One of the strengths of the scientific method, properly employed, is that scientists regularly reject hypotheses. When an hypothesis is rejected, a good scientist reexamines the original observations (is there some detail that was missed, that could provide a better explanation for the phenomenon?), the original questions (perhaps the wrong questions were asked) and looks for alternative hypotheses that can, in turn, be tested by new data.

If you rejected the initial hypothesis, what alternative hypothesis appears reasonable to you?

And if the predictions are met by the data that are collected, does the process stop? No, because hypotheses are, at some level, simplifications of the real world (look back at the questions a and b, above). If an hypothesis is not rejected by an experiment, many possibilities emerge for future research. A scientist can make the hypothesis more detailed and run further experiments to test the new version. Alternatively, the scientist can test whether the proposed explanation holds beyond the initial test population by running other experiments to increase the sample size, moving to other populations, or examining other species.

If your results support the initial hypothesis, what would you consider the next step toward better understanding the mechanism(s) underlying the initial observations, that there are more men in physical sciences than women?

Note that in this cyclic procedure, the investigator is looking for new, better, hypotheses and testing those new hypotheses with new experiments. New data must be collected for each new hypothesis to be tested (although if multiple, alternative, hypotheses with discrete predictions can be developed before the experiment is done, a well-designed experiment can test these hypotheses simultaneously). Each new testable hypothesis is an improvement in our understanding of the world around us. Still imperfect, but nonetheless better than before.

VI. YOUR HOMEWORK: Considering the problems that we discussed with the data collected within the lab group (section IV above), we hope that you can see that the experiment done thus far likely leaves much to be desired, whether it resulted in rejection or support for the hypothesis. The easiest correction for these problems is to increase our sample size. To this end, we wish you to follow the following protocol and collect data from 10 additional people outside of this class. You must find five men and five women.

Protocol - a written set of instructions to be followed, insuring that an experiment or set of observations is done correctly and repeatably.

For each “subject”: a. Determine that they have not done the MI test for another member of the class (having taken it for other purposes such as learning evaluation is acceptable). b. Have each subject take the test alone (do not read the test to them). c. Have each subject fill out their answers on a separate answer sheet (so that you can reuse the test sheet). d. When you are done, tally the total score for men and women separately for each section of the MI test. Email this to your TA by the deadline set, and at the beginning of the next lab section your TA will present these data to you for discussion. Score sheet for Multiple Intelligences Inventory. Biology 1, Fall 2005

Please circle the appropriate answer for this section before starting. 1. I have not taken this test previously for another Bio 1 student T F 2. I am a…. Female Male

Place a “1” next to each number that corresponds with a statement in the test that you feel accurately describes you Section 1 Section 4 Section 7 1. 1. 1. 2. 2. 2. 3. 3. 3. 4. 4. 4. 5. 5. 5. 6. 6. 6. 7. 7. 7. 8. 8. 8. 9. 9. 9. 10. 10. 10. Total ______Total ______Total ______

Section 2 Section 5 Section 8 1. 1. 1. 2. 2. 2. 3. 3. 3. 4. 4. 4. 5. 5. 5. 6. 6. 6. 7. 7. 7. 8. 8. 8. 9. 9. 9. 10. 10. 10. Total ______Total ______Total ______

Section 3 Section 6 Section 9 1. 1. 1. 2. 2. 2. 3. 3. 3. 4. 4. 4. 5. 5. 5. 6. 6. 6. 7. 7. 7. 8. 8. 8. 9. 9. 9. 10. 10. 10. Total ______Total ______Total ______