Chemestiry 100

Experiment #4 Introduction to the Scientific Method

The Skills, Tasks, and Grading Criteria for this assignment are listed below to help students better understand the activity and support successful completion.

Skills we hope you further develop and use in this activity:

● State science related questions clearly.

● Identify and describe variables needed to support your experimental question.

● Organize data into tables and convert to graphs.

● Convert information presented in tables and graphs into words.

● Develop evidence-based conclusions that consider all relevant data, while also acknowledging the limitations of that data or other information.

● Consider how the scientific method can be applied to support your community and/or the environment.

Tasks: ● Read the background sections in order to help you clearly understand science-

based terms (for example, hypothesis) that we expect you to know and use properly. You will also see components for an experimental design that you will need to use in your own experiment that you will design.

● Make sure that you understand the meaning of terms like dependent, independent, and controlled variables, hypothesis.

● Create experimental investigation using the components of the scientific method you learn about from this assignment.

Criteria for success: ● Read questions carefully so you can answer questions completely.

● Use scientific terms that are included in this assignment.

● See rubric for specific scoring criteria at the end of the assignment.

Background Science is the process of applying systematic, logical thinking to understanding the natural world.

In our society, science often gets portrayed as a difficult, confusing subject that only geniuses can handle. This is far from the truth. All people naturally think like scientists when they are trying to understand the world around them. Every time you ask the question What happens when I do this? you are thinking like a scientist.

So why study science? Training in science helps you take a more systematic and logical approach to answering your questions about the world. Scientists often use a process called the scientific method to help them design experiments that answer their questions. One of the goals of this assignment is to help you master the scientific method so that you can use it yourself!

Overview of the Scientific Method

• Observe the World Around You

• Identify a Problem or Question

• Make An Explanation That Can Be Tested (Hypothesize)

• Design an Experiment to Test the Hypothesis

• Analyze the Data Collected

• Draw Conclusions Based on the Data

• Communicate the Results

The scientific method doesn’t always proceed from beginning to end in a straight line. The data that you collect in an experiment may lead you to go back and develop new hypotheses. On occasion, a freak accident during an experiment leads to a completely unexpected discovery! Here’s a visual representation of how the steps of the scientific method can often feed back on themselves:

Practice Using the Scientific Method Let’s practice using the scientific method in a real-world context.

Example 1: Childhood Asthma

Imagine that you are the parent of an elementary school student and a junior high student. You and your children move from one part of town to another, and they start at new schools. As your children start to make new friends and have them over to the house, you make an observation: a number of these new friends appear to have asthma. In fact, you realize that asthma seems to be much more common in students at your childrens’ new schools than it was in students at their old schools.

Based on your observations about asthma rates, you come up with an experimental question:

“Does where children live affect their likelihood of getting asthma?”

From your time studying science at Portland Community College, you know that the next step of the scientific method is coming up with a hypothesis: a possible answer to your research question.

There is a bit of an art to crafting a good hypothesis. When you make a hypothesis, you are saying, “I think that this might be the answer to this question.” You then use your experiment to determine whether or not your predicted answer is supported. Because of this, your hypothesis must be falsifiable: it must be possible to design an experiment that can show whether or not your hypothesis is unsupported.

Examples of hypotheses that are not falsifiable include hypotheses with supernatural explanations (ghosts made it happen) or hypotheses that state subjective opinions (chocolate is a better flavor than vanilla).

Come up with a hypothesis that could explain why more children in Neighborhood A have asthma than in Neighborhood B. Briefly describe an experiment that you could use to test your hypothesis. (That way you know that your hypothesis is falsifiable!)

Hypothesis:

Possible Experiment:

While thinking about your question, you notice that Neighborhood A has a big manufacturing and industrial center, while Neighborhood B does not. This leads you to come up with the following hypothesis:

“Because of unhealthy waste products released into the air by industrial centers, children who live near an industrial center are more likely to have asthma than children who do not live near an industrial center.”

Experimental Design Now that you have a hypothesis, it’s time to design an experiment to test it!

When we conduct experiments, we are usually answering a question like this: “When Thing A changes, what happens to Thing B?” We use the word variable to describe anything that can change in an experiment. We refer to Thing A -- the thing that we think causes something to happen -- as the independent variable in the experiment. We refer to Thing B -- the thing that we think is affected by Thing A -- as the dependent variable. The dependent variable depends on the independent variable; Thing B depends on Thing A.

In this asthma experiment, we are asking, “How does where children live affect their likelihood of getting asthma?” Break this down into cause and effect: we think the cause is where children live, and the effect is that children are getting asthma. Our independent variable is where children live. Our dependent variable is the likelihood of getting asthma.

Revisit the hypothesis and experiment that you came up with on the previous page. What is the independent variable in that experiment? What is the dependent variable?

Independent Variable:

Dependent Variable:

When we design an experiment, we have to decide how we want to measure or categorize our independent and dependent variables. In your experiment, you decide to compare three categories of where children live: within 1 km of the industrial center, between 1 and 3 km of the industrial center, and between 3 and 10 km of the industrial center. You decide to document rates of asthma by determining whether children have seen a doctor for asthma symptoms within the past 12 months.

One of the common mistakes that people make when they first start designing experiments is changing too many variables at the same time. Once you know what your independent and dependent variables are, it is important to keep as many other variables as possible the same. The variables that you try to keep constant in an experiment are referred to as standardized or controlled variables.

In your asthma experiment, you could take ten 4-year-olds who live less than 1 km from the industrial plant and compare them to ten 12-year-olds who live 3 to 10 km from the industrial plant. If you find that the 4-year-olds who live near the plant have much higher rates of asthma, you wouldn’t be able to determine whether living near the industrial plant causes asthma, or whether asthma is more common in 4-year-olds! In order to design a strong experiment, you should keep age (or at least average age) constant between your two groups. You could compare 4-year-olds to 4-year-olds, for example, or you could randomly assign ten children between the ages of 4 and 12 to each group (presumably, each group would have the same average age). Age would then be a controlled (or standardized) variable.

It isn’t usually possible to standardize every variable, but it is important to try to standardize any variables that might have an effect on our experiment. In this experiment, for example, you should think about all the other possible factors that could affect asthma rates in children and try to standardize them.

Think of two other variables that you should standardize in your asthma experiment (two other things about the groups of children that you compare that should be roughly the same).

Controlled Variable 1 Controlled Variable 2

Procedures You have designed your experiment! Now it’s time to carry it out.

When you carry out an experiment, it is important to carefully plan out and record your procedures. The procedures for an experiment are often written out step-by-step, similar to a recipe. A good experiment should be reproducible -- other scientists should be able to repeat what you did and get the same results. Clearly written procedures help make this possible.

Here are the procedures you come up with for your experiment:

1. Send surveys to 1,000 randomly selected children between the ages of 4 and 12

2. Collect surveys

3. Divide the surveys into three groups: children who live within 1 km of the industrial center, between 1 and 3 km of the industrial center, and between 3 and 10 km of the industrial center

4. Calculate the rates of asthma in each group (based on doctor appointments)

Notice that this procedure could be repeated by people near other industrial centers to see if similar results are also found elsewhere.

Data Collection Now it’s time to collect and analyze your data.

There are two primary types of data: qualitative and quantitative. Qualitative data are descriptive, while quantitative data are based on numbers. Whether or not a child has been to the doctor for asthma over the past year (yes or no) is an example of qualitative data. The distance that someone lives from the industrial center (5.3 km) is an example of quantitative data.

When we measure quantitative data, we often record them with an instrument like a thermometer or scale. Quantitative data usually include units; for example, temperature is often measured in , time ℃ is often measured in seconds, and volume is often measured in mL. Ideally, quantitative data are unbiased. Our measurements of quantitative data often contain a small degree of error, but we try to make these measurements as precisely and accurately as possible.

Let’s say you decide to collect data on additional variables during your experiment by including more questions on your survey. Give one example of qualitative data that you could collect and one example of quantitative data.

Qualitative Data Quantitative Data

We often record data using visual tools like tables and charts. Here is a table showing the data from our survey results:

Distance: <1 km 1 - <3 km 3 - 10 km

Number of children with asthma

37 30 34

Total number of surveys returned

233 354 407

Percent of children with asthma

15.9% 8.5% 8.4%

Tables are great for listing lots of data, but it is often easier for us to interpret (make meaning from) those data when they are displayed in a chart or graph. Here is a graph showing the same information in a more visually intuitive way:

Graphs are frequently used in science to more easily visualize the relationship between the variables. The type of graph used depends on the nature of the data collected and the information desired to be communicated with others. Generally, if both the independent and dependent variables are numerical, then a scatter plot is used (a graph with dots for data points). If one variable is grouped into categories (like in our current experiment), then a bar graph is more appropriate (as you can see in the image above).

Regardless of the type of graph used, the independent variable is graphed on the horizontal (x) axis and the dependent variable is graphed on the vertical (y) axis. When making a graph, make sure you include the following components of a good graph:

● A descriptive title

● Labels for the horizontal axis (x-axis) and the vertical axis (y-axis) both with the variable name and units

● Clearly visible data points (or bars)

● An appropriate scale. Most of the time this means that the data points fill as much of the graph as possible

When looking at the graph, we can analyze the data by identifying any observed trends (patterns). To state trends, we often use the following format: As the independent variable changes in this way, the dependent variable changes in this way. Sometimes there’s a clear relationship between the variables, but some experiments do not show any trends.

Take a look at the graph above. What do you observe about the relationship between the rate of asthma and the distance children live from the industrial center?

Conclusions Now that you have your data, you are ready to use them to draw some conclusions.

Remember that we designed our experiment to test our hypothesis. This is the step of the scientific method where we decide whether to accept or reject the hypothesis.

Our hypothesis was: “Because of unhealthy waste products released into the air by industrial centers, children who live near an industrial center are more likely to have asthma than children who do not live near an industrial center.”

Do the results of the experiment support this hypothesis?

Scientists are very particular about using the word “prove”. We never say that an experiment proves our hypothesis correct. This is because the results of a future experiment could show that our hypothesis was not correct. Instead, we say that our results “support” our hypothesis. The more experiments we do that support our hypothesis, however, the more confident we can be in that hypothesis.

Another important thing to note is that an experiment isn’t a game where you get a prize for guessing right. It is totally okay for your hypothesis to be incorrect! We often learn more from results that we don’t expect. A hypothesis is simply a starting point. You should never change your results to match your hypothesis. You can, however, make a new hypothesis based on the results, and test this hypothesis in a new experiment.

Communicating Your Results Now that you have completed your experiment, you need to share your results with other people! To build confidence in your conclusions, it is important to have other scientists “peer review” them alongside your experimental methods and results. Different perspectives can provide insight about how to strengthen your evidence.

Scientists should share their information, insights, and analytical skills to improve their communities. In the case of this experiment, your results have serious potential health implications for people who live near this industrial center. Who should hear the results of your research? How could you communicate your results with those people?

Next Steps

You just found indications that the industrial center in your neighborhood may be related to asthma rates in children. How can you further test your hypothesis that this is due to waste products in the air?

You might now design an experiment that analyzes what pollutants are in the air at different distances from the industrial center. You could also decide to look at another factor that may be influencing asthma rates in this area, such as poorer quality housing near the center. The scientific method is often like a spiral staircase: Each new experiment builds upon the results of the one that came before it.

Take a moment to reflect upon the ramifications of this experiment. How does this connect to and impact your life?

Example 2: Baking a Cake (Optional)

If you would like, you can look at another short example of how the scientific method is used to design an experiment. Suppose a baker is trying to improve their cake recipe. They want to make their cakes come out of the oven fluffier, and they know from previous observations that the number of eggs has some effect on fluffiness. The cook realizes that fluffiness itself is difficult to measure, so in its place they will measure the thickness of the cake when it comes out of the oven. Since the chef always uses the same volume of batter in the same size pan, the fluffier cake will be the thicker one.

Practice identifying the research question, independent variable, dependent variable, and hypothesis in this experiment. (The answers are just after.)

Research Question:

Independent Variable:

Dependent Variable:

Hypothesis:

Research Question: How does the number of eggs in the batter affect the thickness of the cake?

Independent Variable: Number of eggs (intentionally manipulated)

Dependent Variable: Thickness of the cake (measured in response to independent variable)

Hypothesis: Because there is air in beaten eggs, increasing the number of eggs in the batter will increase the thickness of the cake.

Notice that the hypothesis is in the form of a statement (not a question). Also, the hypothesis has the ability to be proven false.

Next, the chef is going to design an experiment to test the hypotheses. Since the independent and dependent variables are already defined, the chef needs to identify the variables to keep constant.

Controlled Variables (kept constant):

● All ingredients in the recipe, except for the eggs (1 ½ cups cake flour, 1 cup sugar, 2 tsp baking powder, ¼ tsp salt, ½ cup milk, and 3 oz unsalted butter)

● Pan size (9-inch round)

● Oven temperature (375 °F)

● Cooking time (35 minutes)

The experimental plan is to vary the number of eggs for each trial for the cake and to measure the thickness of the cake after it bakes in the oven.

The baker completed this experiment, recorded observations, and summarized the experimental results in the data table and graph shown below.

Table #1 Cake Thickness and Number of Eggs

Trial

Number of Eggs (Independent Variable)

Thickness of the Cake (Dependent Variable)

(cm)

1 1 4.1

2 2 3.8

3 3 2.7

The baker then looked for trends in the data.

What relationship do you see? (Try answering on your own before reading the information below.)

Should the baker accept or reject their hypothesis based on these results? (Try answering on your own before reading the information below.)

Data Interpretation: As the number of eggs added to the batter increased, the thickness of the cake decreased.

Conclusion: The data from this experiment did not support the prediction that an increase in the number of eggs (independent variable) increases the thickness of the cake (dependent variable). Therefore, the hypothesis was not supported: The extra air in the beaten eggs did not contribute to the fluffiness of the cake. A different ingredient in

the recipe would now need to be varied or the effect of varying the temperature of the oven would need to be investigated to see whether these variables affect cake fluffiness. One final note, eating the cake with only 1 egg did not taste very good, even though it resulted in the thickest cake!

Assignment In this activity, you will come up with an experiment that you can do at home. The possibilities are endless! You will make an observation that leads you to ask a question. You will then develop and test a hypothesis, analyze data, and draw conclusions about the experiment that you performed. You will follow the scientific method, just like you practiced in the experiments above. Have fun with this! If you pick an experiment that you are actually curious about, you will get a lot more out of this assignment.

Scientists often repeat experiments to make sure that they get the same results each time. Each repetition is called a trial. You will perform at least three trials to test your hypothesis (in other words, you will repeat the experiment at least three times).

Safety

Choose an experiment that will not cause any injury to you or anyone in your household. If you choose an experiment with safety risks, such as using an oven, make sure to take proper precautions to ensure the safety of yourself and everyone around you.

Procedure

Follow the procedure below and write all your answers on the report sheet found on the next page.

1. Propose a new question that you would like to know the answer to or investigate.

Examples:

● Does my dog prefer beef treats or turkey treats?

● Does the pressure of air in a basketball impact the height that it bounces when dropped from 3 feet?

2. Propose a hypothesis.

3. Identify the independent, dependent and controlled variables for your experiment.

4. Procedure: Write the steps you will follow to complete your experiment. They should be detailed enough so that someone can follow them, but not so specific that you spend more than 15 min writing them out.

5. Create a data table for results.

6. Complete your experiment and record your observations on the report sheet.

7. Graphically present your data using the guidelines in the introduction.

8. Interpret your graph, stating any apparent trends in complete sentences.

9. Complete the rest of the report sheet.

Tasks 1. Read the background sections in order to help you clearly understand science-

based terms (for example, hypothesis) that we expect you to know and use properly. You will also see components for an experimental design that you will need to use in your own experiment that you will design.

2. Understand the meaning and use the terms dependent, independent, and controlled variables, hypothesis, and trend.

3. Create a valid experimental question for investigation.

4. Write a hypothesis which includes a specific prediction of how the dependent variable will be affected by the independent variable along with reasoning.

5. Design an experiment with sufficient detail in procedural methods.

6. Analyze experimental data graphically, identify trends, and draw valid conclusions based on the data.