Look at the photo below. Do you believe what you see?

The image is of a picture taken in 1967 of a supposed bigfoot or sasquatch.

Source: Roger Patterson's Sasquatch, History Link

On October 20, 1976, Roger Patterson and Robert Gimlin set out on horseback to explore the northern California wilderness and search for Bigfoot. Using a 16mm hand-held movie camera they shot the following footage.

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Source: Roger Patterson Bigfoot footage, DavidWalker1964

The film has been studied by many scientists throughout the world who continue to remain divided on the authenticity of the sighting.

When we are asked to evaluate scientific explanations, we have to evaluate or judge the reliability of what we hear and see. We have to use critical thinking to evaluate explanations. In order to think critically, you must combine your prior knowledge with newly learned facts or observations.

Evaluating scientific explanations involves several steps. For example, you should evaluate the methodology that was used. Two steps are shown below. Click on each step to learn more.

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Step 1: Evaluate the Data

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Step 2: Evaluate the Conclusions


  • Is the data specific? Data given to back up a claim needs to be exact. Notes and observations made should be detailed and clear. Notes should be made during the investigation, not after the fact.
  • Can the data be repeated? Scientists require repeatable evidence. Other scientists should be able to repeat the investigation and get the same results. When evaluating data, look to see if other scientists have repeated the data. If not, the data might not be reliable.

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  • Does the conclusion make sense? For the conclusion to be reliable, it must be plausible.
  • Are there any other possible explanations? Conclusions are not valid unless any other possible explanations are proven unlikely. NOTE: Reliability is linked to repeatability. Does repetition of the investigation yield the same or compatible results in different clinical experiments or statistical trials? Validity is the extent to which a test measures what it claims to measure and whether the data yielded is relevant to the question.
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