Demystifying myths with data

This exercise is a part of Educator Guide: Pink diamonds and Demystifying myths with data / View Guide
A 1934 photo supposedly of the Loch Ness Monster.
The existence of Nessie, shown in a 1934 photograph that turned out to be a hoax, probably can’t be explained by a giant eel living in Loch Ness, a new study suggests.Keystone/Stringer/Hulton Archive/Getty Images

Directions for teachers

Ask students to read the Science News article “Seen Bigfoot or the Loch Ness Monster? Data suggest the odds are low” and work with a partner or in groups to answer the first set of questions. Then have students work individually, with a partner or in groups to answer the second set of questions. As an option, students can complete their research study on a myth of their choice.

Directions for students

Read the Science News article “Seen Bigfoot or the Loch Ness Monster? Data suggest the odds are low” and answer the questions as directed by your teacher.

Myths and data

1. What do you know about myths? Think of other classes where you may have encountered myths. How do they originate?

Students may have encountered myths in history or English classes. Myths often originate to explain a phenomenon that is not understood or to influence behavior.

2. How is a myth similar to or different from a scientific theory or hypothesis?

Myths and scientific theories or hypotheses all seek to explain a phenomenon, but myths are not tested. Scientific theories are based on data, and hypotheses are designed to be tested, whereas myths are merely believed.

3. Based on the article, what is your understanding of data science?

Data science is the use of data to answer questions, such as the probability of something occurring.

4. What was the myth that data scientist Floe Foxon investigated?

Floe Foxon used an existing data set to investigate whether the Loch Ness Monster could exist.

5. To come up with a scientific question, what possible explanations for the myth did the data scientist explore?

The scientist explored whether the Loch Ness Monster sightings could have been explained by spotting another animal, such as a giant eel, in the lake.

6. The approach to answering scientific questions should be measurable and controllable, which means that an investigation using observation, scientific tools or computer simulations should be able to help answer the questions. Scientific answers are supported by empirical evidence gathered through research, experimental or engineering processes. What is one scientific question that could have been asked by the data scientist to inform the study described in the article? Explain the data he used to study his question.

What is the probability of finding a 1-meter-long eel or longer eel in the lake? The scientist used data on eels caught in Loch Ness and other surrounding waters, since eels are thought to be an explanation for sightings of the Loch Ness Monster. He analyzed the data on eel length and used it to calculate the odds of finding eels of differing sizes.

7. What did the data scientist conclude? Did he “bust” the myth and reveal whether the Loch Ness Monster was real? Explain.

Based on the data he analyzed, Foxon concluded that the chances of finding an eel 1-meter-long was almost zero. A sea creature as long as the legendary Loch Ness Monster has an even smaller probability. Student answers will vary about whether the myth was “busted” or not. While the scientist answered his scientific question about the probability of finding eels of certain lengths in the lake, he didn’t prove that the Loch Ness Monster didn’t exist. That would require looking at specimens.

Measuring up a myth

1. Think of a myth that you’ve heard. Based on your knowledge, what are probable or possible explanations for the myth?

Student answers will vary. One example could be the myth that lightning never strikes in the same place twice. A possible explanation could be that it’s highly unlikely that lightning would strike in close proximity more than once in a person’s lifetime.

2. What is a scientific question you could ask that could try to explain the phenomenon? (Refer to question 6 above for the definition of a scientific question.) Write a scientific question to determine the truth behind the myth.

What is the probability of a lightning strike in the same 50-meter radius area more than once in 100 years?

3. Explain the design of your research study. Will you collect data yourself or analyze an existing data set? What are some possible limitations of your research study?

To test my question, I could collect data on lightning strikes and analyze the probability of lighting striking in a 50-meter radius area in 100 years. It would likely be difficult to analyze data all over the Earth, so I’d have to narrow down the focus to specific areas where data exists. I may also have to adjust the time period of the strikes studied. There are publicly available data sets on lightning strikes, like the Severe Weather Database Inventory Lightning Tile Summaries.

4. Once you collect and analyze your data, how could you go about sharing it with others? Do you think your results would dispel the myth? Do you think it would be an easy or a difficult process to get communities to dispel myths? Explain.

I could share my results with my community. But myths are often deeply ingrained beliefs, so it’s not an easy process to change people’s minds, even with evidence. Also, the data studied to answer one scientific question will not provide a completely comprehensive analysis of every possibility, so additional scientific questions may need to be studied.  

5. Write a brief description of how to “bust” a myth.

Determine possible and probable solutions for a myth, then create a scientific question to investigate through observation, experimentation or engineering design. Collect and analyze relevant data to prove or disprove the scientific question. Repeat with additional scientific questions, as needed.