This lesson teaches how to make inferences by observing data. It begins with defining inferences and uses examples like sports teams, lawn care, income histograms, and geyser eruptions to illustrate how observations and prior knowledge lead to inferences. The lesson emphasizes that inferences are not always correct but should be based on observations. It concludes by reinforcing the definition of an inference as a statement derived from observations and prior knowledge.

What is a key component of making an inference?

• Relying solely on predictions

• Guessing based on feelings

• Ignoring data

• Observations and prior knowledge

Why might an inference about a lawn not always be correct?

• Because it is based on incorrect data

• Due to factors like bugs and soil conditions

• Because it is not based on any observations

• Due to the lack of water

What does a histogram help you understand about data?

• The historical context of the data

• The color of the data

• The distribution and range of data

• The exact future outcomes

What can be inferred about earnings from the histogram example?

• The mean is irrelevant

• Everyone earns the same amount

• Most people earn between $10 and $40 an hour

• Earnings are not influenced by education

What does the box plot example suggest about wait times for Old Faithful?

• Wait times are unpredictable

• Wait times are always exactly 78 minutes

• Wait times likely fall between 64 and 80 minutes

• Wait times are irrelevant