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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