The Nature of Science and Experimental Design

Instructions: Complete the following activities prior to attending lab. TYPE YOUR ANSWERS, print it, and turn it in by the beginning of lab on Thursday. This is individual work, answers should all be independently constructed.

Part 1: Questions, hypotheses, and predictions

Scientific investigations often have their origins in direct observations of living things or in reading reports of work by other scientists. Knowledge constructed through observation, testing, and measurement is termed EMPIRICAL. Initial observations lead to questions that require additional investigation. Tentative answers to a scientific question are called hypotheses; hypotheses, in turn, naturally lead to testable predictions. Predictions follow from hypotheses and indicate what outcome will be expected in an investigation if a particular hypothesis is true. Predictions can be (but don't have to be) formulated as if....then statements.

Scientific Questions:

Besides the need to be empirical, "good" scientific questions should possess a number of important characteristics. Some of these characteristics overlap with those required to be empirical, others allow for a reasonable attempt to be made at answering a meaningful question. Remember, a "good" scientific question leads to the formation of falsifiable hypotheses, which leads to the formation of testable predictions. Scientific questions should be testable, measurable, controllable (if experimental), definable (question is not too broad), and applicable (question has some meaning). For example, the question "How does temperature affect the growth rate of bacteria?" is a pretty good scientific question (it could be better defined with specific temperatures). Here are a few examples of bad scientific questions. For each question, determine what is wrong with the specific question and provide an improved version (or related version) of the question. An example is provided.

1. EXAMPLE: How many organisms are affected by plastics?

Not definable (too broad/vague). What proportion of seagulls observed at Newport Pier ingests plastic?

2. How much plastic is under the bench outside?

3. Is plastic found on the street worse than plastic found on the grass?

4. Why is there trash on campus?

Hypotheses& Predictions:

Hypotheses are tentative answers to scientific questions. In addition to possessing all the important characteristics of a good scientific question, and being able to lead to a testable prediction, hypotheses must also be falsifiable, meaning it is possible to disprove them. An example of a hypothesis that is not falsifiable is "The temperature in Death Valley can reach 65°C". It is impossible to determine if there is no combination of circumstances will cause the temperature be that high, so we can never prove it wrong. A similar hypothesis can be proposed that is falsifiable "The temperature in Death Valley cannot reach 65°C". This example is falsifiable...it would only take one record-setting hot day to refute the hypothesis and disprove it. Consider the following examples and indicate whether they meet all the characteristics of a scientific hypothesis.

To better understand these concepts, consider the following example. In many parts of the country, when daily temperatures drop in the fall and day length decreases, some trees undergo color changes in their leaves and eventually shed them. One might ask:

Q: Are seasonal changes in the environment responsible for causing leaves to drop?

To tentatively answer this question, one or more hypotheses can be developed:

H₁: the decline in average daily temperature causes leaves to turn different colors & be shed

H₂: the decline in day length triggers changes that cause leaves to turn colors & be shed

H₃: A combination of both external factors brings about the color changes & shedding.

One prediction that emerges from these hypotheses is:

P₁: (If H₁ is correct...)If plants are exposed to decreasing temperatures over several weeks while keeping day length constant, then the leaves should result in color changes.

What other predictions would you make from these hypotheses?

P₂: _____________________________________________________________________

P₃: _____________________________________________________________________

The following examples include good and bad hypotheses. If possibleand necessary, improve upon the hypothesis; then write a testable prediction for each. An example is provided.

1. EXAMPLE: All the cells in a human body possesses the same genome.

(Can't create a testable prediction...too many cells to test all of them!)

Improved: Cells in multiple tissues from the human body possess the same genome.

Prediction: Cells sampled from the liver, skin, and intestines will have identical genomes if sequenced.

2. Whales feel emotions like love and sorrow.

3. Queen palm trees use more water per unit mass than redwoods.

4. The diversity of animal life on Earth is the result of intelligent design.

5. More pizza is consumed by college students on nights with a full moon.

Part 2: Variables

Read the material below and answer the questions that follow.

Hypotheses state a tentative relationship. In H1above it is the relationship between daily temperatures and change in leaf color. What is it for H2and H3?

H₂: ___________________________________________________________

H₃: ___________________________________________________________

For H₁ the two related factors, called variables, are average daily temperature and second, leaf color change. The temperature is called the independent variable. It is the one the scientist controls or that varies on its own during the experiment. The other, color change, is a dependent variable, which according to the hypothesis is "dependent" for its expression on the independent variable. Another way of saying it is that the changes in the independent variable cause changes in the dependent variable. The idea of a "variable" comes from the fact that both temperature and color change can vary in their expression or value. Temperature can have a range of values; color change values can occur or not occur, or occur at a particular rate, or occur to different degrees (e.g. different colors). Hypotheses, then are statements about possible relationships between independent and dependent variables.

For H₂and H₃,determine what the independent and dependent variables are and indicate what range of values they might take.

There is a third important type of variable which can have great influence on the outcome of a scientific investigation, the nuisance variable. Nuisance variables are factors that, in addition to the independent variable, can influence the dependent variable and thus, the outcome of the study. These also may be called controlled variables or constants, because scientists try hard to control or standardize them so as not to confound their results. Consider the study of the relationship between temperature and leaf color. If in an investigation during the fall, day length shortened as temperatures dropped over several weeks, it would be difficult for the scientist to determine which factor caused the change in leaf color. For this experiment, day length is a nuisance variable and needs to be controlled in order that the results are not confounded. There are many other nuisance variables and ways of dealing with them that are dealt with in the next section.

Relationships between variables:

In Biology, many of our questions pertain to relationships between two or more variables, such as blood pressure and heart disease, or elevation and reproductive output in birds. The following are variables with potential relationships. For each pair of variables:

a. Write a question about the relationships

b. Write two or more hypotheses (possible explanations) to tentatively answer the questions

c. Indicate the independent variable and a range of values it might take

d. Indicate the dependent variable and a range of values it might take

e. Make a prediction about the outcome of a scientific investigation to test one of your hypotheses

Variables:

1. Studying and grades.

2. Bacterial growth and temperature.

3. Muscle size and muscle strength

4. Select your own two variables

Part 3. Constants and Controls

Read the material below and answer the questions that follow.

A control is any means used to eliminate or minimize factors that might confound or obscure the relationship between the independent and dependent variable in a scientific investigation. We provided just one example in the previous paragraph, but every good study has several or dozens of controls built into it. Consider the following:

A microbiologist wants to investigate the relationship between antibiotic resistance and antibiotics in animal feed. She hypotheses that antibiotic resistance increases in animals given antibiotics in their food. Specifically, she predicts that " If animals have increasing levels of antibiotics in their feed, then there will be an increase in antibiotic resistant microbes." The dependent variable, number of antibiotic resistant microbes, can be measured in several ways. For example, the microbiologist can determine the different species of antibiotic resistant species, estimate the actual number of antibiotic resistant microbes in a population, etc. In carrying out the experiment, the amount of antibiotics put into the food (independent variable) is varied systematically. All other potential variables, such as species and breed of experimental animals, type of food, type of housing, water quality, temperature in the environment, etc. must be standardized. If they are not controlled, these nuisance variables may effect the outcome of the experiment and confound the relationship between the independent and dependent variable. The means of standardizing or eliminating nuisance variables are called controls.

To check your understanding of controls, suggest how each of the following potential nuisance variables could be controlled.

1. species tested _________________________________________________________

2. breed of species tested __________________________________________________

3. diet _________________________________________________________________

4. water quality __________________________________________________________

5. temperature in the living quarters __________________________________________

6. amount of space for each animal ___________________________________________

Controltreatment groups are another form of control that is used in most scientific investigations to protect the researcher from drawing an erroneous conclusion. Control treatment groups come in two forms- negative controls and positive controls.

A negative control group is the "classic" control that most people think of: the independent variable is eliminated or set to a standard value, providing a comparison to the other treatment groups. An example of a negative control group in the animal feed experiment could be a group that is fed feed with no antibiotics. A negative control group controls for the occurrence of false positives. False positive results are the presence of an observed treatment effect when there isactually no treatment effect. These are typically caused by experimental error or the influence of some variable that was not taken into consideration during the experimental design. If the bacteria became more resistant in the negative control animals (no antibiotics in the feed), in addition to the treatment animals (antibiotics in the feed), what might you conclude?

How does this differ from your likely conclusion if you had not included the negative control group, but observed antibiotic resistance in treatment animals?

A positive control group helps to insure that you can actually detect a treatment effect, if it truly exists, thus allowing the detection of false negatives. False negatives are the apparent absence of a treatment effect when there actually is a treatment effect.A false negative can occur for a number of reasons, but often occur due to a lack of sensitivity in a test, a failure of the method itself, high variability in the data, or poor replication (low statistical power). Assuming there is a treatment effect in this example experiment (i.e. antibiotics in feed result in more resistance bacteria), an example of a false negative would be no growth of any bacteria in any treatment group. An example of a positive control could simply be to grow bacteria on plates with no antibiotics, proving your method of bacterial culture is successful. What might you conclude if you did not observe any bacterial growth on plates without antibiotics, in addition to an absence of growth with antibiotics?

How does this differ from your likely conclusion if you had no included the negative control group?

In most experiments, there are often more possible positive controls than negative controls. Give an example of another positive control that could be useful in this experiment.