Q.Consider the temperature (in Celsius) of 7 days of a week as 34, 34, 27, 28, 27, 34, 34. Identify the appropriate statistical technique to be used to calculate the following:

Two batsmen both average 50 runs. One scores 48, 52, 49, 51 — steady as a clock. The other scores 0, 100, 5, 95 — brilliant or useless, never in between. The average alone cannot tell them apart; what separates them is consistency, and the number that measures consistency is the standard deviation. It answers one question: on a typical day, how far do the values stray from their own average?

The procedure builds that answer in honest steps:

• Compute the mean of the values.
• For each value, find its deviation — how far it sits from the mean. Some deviations are positive, some negative.
• Square each deviation. This does two useful things: it makes everything positive (so ups and downs cannot cancel each other out), and it punishes large strays more than small ones.
• Average the squared deviations. This average is called the variance.
• Take the square root, which brings the answer back into the original units — runs, rupees, marks — instead of squared units.

So the standard deviation is, in plain words: the square root of the average squared distance from the mean. A small value means the data huddles close to its average (the steady batsman); a large value means it swings wide (the erratic one). A standard deviation of zero means every value is identical.

Python computes it directly:

import statistics
scores = [48, 52, 49, 51]
print(statistics.pstdev(scores))   # population standard deviation
print(statistics.stdev(scores))    # sample standard deviation
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