Random sampling in hypothesis

Random sampling in hypothesis
Conclusions on hypothesis are determined by the key factors, probability theory and sample evidence. The factors are used to assert the viability of the hypothesis statement. A random sample gives an opportunity for the population to participate equally. Random sampling does not portray bias to any part of the population (Duttalo, 89). Convenience sample does not give a true analysis of the population because researchers take what is convenient with them. Probability has elements of truth as it uses evidence of repeatability to measure a population. Therefore, it is important to use random sample to enable the ease of making comparison in the null hypothesis of a population.
Random sampling receives support from probability mathematics whose facts have been proved beyond doubt. Convenience sample does not use probability in arriving at the final sample. It is not suitable for assessing a population. Due to its ‘ non-probability’ nature, convenience sampling is not suitable for studying the relationship between a population and sample. Random sampling gives a true representation of the population because it is not biased to any side. The use of Convenience samples makes researchers choose samples that they prefer but not a true representation of a population (Duttalo, 123). It gives a conclusion that is not true about the population. In statistics, bias should not be given to any part of a population. A true hypothesis should not have bias. For example, in a data that contains a population of one hundred, it is wrong to select the first ten elements to represent the population. The best representative of the population should involve random sampling. Random sampling requires that one chooses any ten elements from the population without taking any consideration on the position.
Work cited
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Dattalo, Patrick. Strategies to Approximate Random Sampling and Assignment. Oxford: Oxford University Press, 2010. Print.
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