Island 6 
Vernier Science:
Digital Data

Keep in Mind

The challenge is to conduct experiments of your choice using two different Vernier sensors and Logger Lite software.
How do we use sensors and other measuring tools to conduct experiments? Why do scientists collect data? 
Explore how digital information can be used to learn more about the world around you.

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Vernier Science: Digital Data

The Scientific Method

When doing science experiments, it helps to have an organized system that everyone follows. We call this the scientific method. By following this process, scientists have a standard way of finding answers to new questions.

There are 5 steps to the scientific method:

Step #1: Ask a Question

What is the purpose of your experiment? When coming up with your question, think about what you want to find out. It should be specific and testable.

Example: The question, "Which M&M candy color is better?" is a hard question to test. What does "better" mean? How can we test that? This question will have a different answer depending on who you ask.

A testable question would be, "What is the most popular M&M candy color at my school?" This can be tested by asking the students at your school which color they prefer and adding up the votes. 


Step #2: State a Hypothesis

A hypothesis is a guess about the answer to your question. It is not important whether your hypothesis is correct or incorrect. The purpose of a hypothesis is to have a prediction you can test with an experiment.

It is a good idea to do some research about your question topic beforehand to help form a well-educated hypothesis.

Example: A hypothesis for our M&M's question might be something like this: "Based on student surveys done at other schools, we think green M&M's will be the most popular candy color at our school." This is an educated prediction about the original question.


Step #3: Do an Experiment

Test your hypothesis by collecting data through an experiment. This is where you gather and record information to answer your question.

Example: To test our M&M hypothesis, we could make a survey that asks each student at our school which M&M color they like best.

A Note on Data Accuracy

At this stage, you may realize that the experiment you designed does not answer your scientific question accurately or fully. If this happens, it is okay! This is just part of the experimentation process. Redesign your experiment and try again.

Example: What happens if you share your survey with your classmates and some of the students fill out more than one? Or what if students choose more than one favorite M&M? Your data would not be accurate. One solution would be to create a new survey that ask students to choose only one answer.  Then, get a list of all the students who go to your school. When a student returns the survey, mark him or her off of the list so you can be certain that each student submits only one response. 


Step #4: Analyze Your Data

Study the data you collected from your experiment. Now that you have gathered more information, you can organize it and find any interesting patterns or trends. This can be done with a graph or another visual representation. How does the information help answer your question?

Example: After the students at your school answer the survey, you could take the data and make a bar graph or pie chart to show the results. Once you make a visual, you can easily see which colors are most popular.


Step #5: Draw a Conclusion

Compare your data to your prediction. Does the data support your hypothesis? Why or why not? Use evidence from your experiment to support your conclusion.

This is also a good time to reflect on the experiment and mention possible sources of error that were not already fixed during step 3. Was there an obvious mistake made during part of the experiment? Was there something important that was overlooked? Nobody is perfect, and neither is every experiment. Knowing where things might have gone wrong or how things could be done better helps improve future experiments.

Example: A conclusion statement for the M&M experiment might look like this: "Based on the experiment, half of the school preferred red M&M's, making it the most popular color overall. This goes against our hypothesis that green M&M's would be the most popular. After collecting survey results, we found out that almost all of the 4th graders were gone on a field trip and could not take the survey. This may have affected the survey results." 


Collecting Data

What kinds of science experiments have you done? 

You have probably measured something such as the temperature using a thermometer or the change in a plant's height using a ruler.


The information we collect is called data, which can be used to understand experimental results. There are many tools we can use to measure data accurately. Tools are chosen based on the type of data you want to collect to test your hypothesis.

Modern technology allows us to collect a type of data called digital data using electronic sensors. Sensors take precise measurements that are easy to compare. Avoid using your own senses (sight, touch, smell, hearing, and taste) to measure data because it often results in inaccurate data. For example, by using your sense of touch, the best you could do is to sense if an object "feels hotter" than another, but you could not accurately identify how much warmer. To get accurate measurements in degrees, you should use a temperature sensor, such as a digital thermometer.

We can then use computers to record and help us analyze this digital data. Some electronic sensors, like the ones you will use in this Liftoff Challenge, can be connected directly to a computer. We can automatically record data over a period of time, and we can use computer software to quickly create graphs and analyze our experimental data.