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Conditionals: Evidence and Measurement

≈ 30 min
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Evidence and Measurement

Connect the claim to observable evidence and state what uncertainty or limitation remains.

This extension applies that lens specifically to Conditionals.

Conditionals run code only when a condition is true:

if age >= 18:
    print("adult")
elif age >= 13:
    print("teen")
else:
    print("child")

The comparison operators produce a bool: == equal, != not equal, <, >, <=, >=. Indentation (4 spaces) is how Python knows which lines belong inside the if.

Code practice

Python Foundations — Evidence and Measurement: Write classify(n) that returns "positive" if n is greater than 0, "negative" if less than 0, and "zero" otherwise.

python

Runs in your browser. Charts (matplotlib) are not supported — use print-based output.

Code practice

Python Foundations — Evidence and Measurement: For the given age = 20, set is_adult to a bool that is True when age is at least 18.

python

Runs in your browser. Charts (matplotlib) are not supported — use print-based output.

Python Foundations — Evidence and Measurement: What does the expression 7 != 3 evaluate to?

Name the original topic being extended by this evidence and measurement lesson.

Which statement is the best evidence-led starting point for Conditionals: Evidence and Measurement?