Page 150 - Data Science class 10
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2.3.4. Interpret the Results
After you analyse your data, you must interpret it in order to provide an answer — or answers — to the original
question.
This step is also known as looking beyond the data and allowing for variability. Variability is present and must be
taken into account when making statistical judgements. When interpreting the results of a randomised comparative
medical experiment, we must remember there are two important sources of variability: randomisation to treatment
group, and variability from individual to individual. When we generalise the results and look beyond the study data
collected; we must consider these sources of variability.
Problem 1: Variability of Measurement
Let’s say you’d like to find out the length of the room you’re in.
1. Ask a Question
How long is the room?
2. Collect Data
Collecting data to help answer the question is an important step in the process. You obtain data by measuring
something, so your measurement methods must be chosen with care. Sampling is one way to collect data;
experimentation is another.
Example of Collecting Data
Measure the length of the room in centimeters, using two different measurement devices: (1) a one-foot or
equivalent ruler and
Measure the room length five times with each device, and fill in the tables below. Record your measurements to
the nearest inch.
Measurement Room Length (in Inches)
Instrument
Ruler
Yardstick
• Are the five measurements you obtained with the ruler exactly the same? Can you explain why there may be
differences?
• Are the five measurements you obtained with the yardstick exactly the same? Can you explain why there may
be differences?
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