Hypothesis testing in statistics is where an analyst carries out a test on an assumption concerning a population parameter. The hypothesis test for use depends on the nature of data one deals with and the reasons for that analysis. It is useful in testing the plausibility of a given hypothesis using a data sample Holmes, Illowsky & Dean, 2017). A simple hypothesis study that can help make a given conclusion would be an experiment involving the flipping of a coin. It would be assumed that a fairly balanced coin has an equal chance of resulting in a head or a tail. If it is not balanced, the chances of getting ahead or a tail cannot be equal. The null hypothesis would be that half of the flips could result in tails and the other half in heads. The alternative hypothesis can be that the sum of tails and heads could be dissimilar. In this experiment, coins will be tossed n times, and this procedure can be conducted repeatedly to an infinite number of times.
The experiment here is flipping the coin 200 times, and because it is assumed that the coin if fairly balanced, the expected number of Heads or Tails is 50 or thereabout. The fewer the number of flips, the more the chances of the coin being biased. The more the flips, the lesser the chances of the coin being biased, and this is only possible when the coin is balanced as assumed. This experiment would test the random variable, and an example include how many times the tail will appear, how many times the head will appear, the number of flips needed to make the head appear. These variables are random because their values are not known (Beaumont, (2005). If I flip the coin 200n times as stated and end up with 160 tails and 40 heads, there is a clear inclination to reject the null hypothesis from the data. The evidence from this data would help in concluding that the coin is not fairly balanced.
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References
Beaumont, G. P. (2005). Probability and Random Variables . Cambridge: Elsevier Science.
Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics . Rice University.Huston Texas.
