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Hypothesis testing - Essay Example

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There are two types of hypothesis i.e. null hypothesis and alternate hypothesis (Pickard, 2007). The null hypothesis assumes that there is no significant variation in the…
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HYPOTHESIS TESTING Hypothesis testing is the statistical determination of probability that a given hypothesis is true. There are two types of hypothesis i.e. null hypothesis and alternate hypothesis (Pickard, 2007). The null hypothesis assumes that there is no significant variation in the statistical observation. The null hypothesis is stated to be true unless disapproved by the alternative hypothesis. Alternative hypothesis on the other hand is the hypothesis that there are random influences on the observation. Acceptance of the null hypothesis automatically leads to the rejection of the alternate hypothesis.
There is a procedure followed in hypothesis testing. The first step involves the stating of both the null and alternate hypothesis. The hypothesis is stated in a manner that they are mutually exclusive. The second stage is the determination of the level of significance (Lehmann, 2004). The higher the level of significance, the higher the precision attained. The sample size is then determined to allow for the collection of data. The size of data will determine the type of test conducted.
After the collection of data, one moves to the stage of data analysis. In this stage, the value of test statistics is calculated e.g. z-value or the t-value (2012). The analysis of the calculated value is made by use of the statistical tables. The table is used to determine if the calculated score falls within the acceptance or rejection region (Lehmann, 2004). The final stage is then made depending on the location of the score. If the calculated value falls in the rejection region, the null hypothesis is rejected implying that there is significant variation in the observation made.
Two kinds of error can be made in hypothesis testing: type one and type two errors. Type one error is that which leads to the rejection of null hypothesis which is true whereas type two error is the error of accepting a false null hypothesis. In conclusion, the stages in hypothesis testing if well followed makes the hypothesis testing easy to comprehend and make statistical decisions. Hypothesis testing is therefore essential in analyzing data to determine its validity and truthfulness hence important in making statistical decisions.
List of References
Hypothesis Tests. (n.d.). Statistics, Probability, and Survey Sampling. Retrieved March 21, 2012, from
Lehmann, E. L. (2004). Testing Statistical Hypotheses. New York: Wiley.
Pickard, A. J. (2007). Research Methods in Information. London: Facet. Read More
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