By taking the first ten students and the last ten students, the sample was almost evenly distributed.
Here, the students were chosen systematically. The sample of students picked were those falling between ids 21 to 40. This was because the number of days absent was evenly distributed. Again, there was need for an average that is closed to the total population’s average.
Convenient samples can easily be assessed; they are not involving. In this sampling technique, data is easily gathered and analyzed. A major risk associated with this method is that it is not representative of the whole population (Thompson p16). Respondents can sometimes be biased. Moreover, there may be overrepresentation and underrepresentation of some members of the sample.
Simple random sampling has a major advantage; respondents are selected randomly, so the results may be close to average. Every segment has an equal probability of being chosen. It reduces biases associated with overrepresentation and underrepresentation (Thompson p24). A major disadvantage of this method is that all members of the population may have to be listed; which could be quite cumbersome and time consuming.
Systematic sampling uses fixed intervals with a stated staring point. It has the same advantages and disadvantages as simple random sampling. In all the three sampling techniques, the sample interval was ten.
When a larger number is considered (40 instead of 20), the variation reduces. The average values are closer to the average for the total than when a sample of 20 is used. For instance, the largest variation is 7.1-5.85=1.25, and the smallest is 5.85-5.5=0.3. Larger samples have less variation. They give better results, which mean the average are closer to the whole population’s average. It leads to generalization of the whole population (Thompson 34). Systematic sampling improves estimates since it representative samples are considered for best