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A speculated statement that evaluates a study outcome is called the research hypothesis. By implication, an experimental design is under a research hypothesis. Thus, a research hypothesis can be evaluated with prediction, deduction, induction, test of predictions and observation. However, a research hypothesis for an experimental design must be testable or falsifiable. To understand hypothesis testing, we must describe the null hypothesis. The null hypothesis is the accepted fact of a research study. As a result, a statistical test must align with the null hypothesis. The z-test is a statistical method for evaluating a normal distribution. As a result, statistical values with a large population can be evaluated using the z-test.
The population variance and large sample size influences support the z-test. Thus, the elements of experimental design include the working mean, population variance or deviation, population normal distribution, and the sample size. By implication a researcher must use the z-test to evaluate proportion studies (Rao, 2007). It is convenient to use the nonparametric test for non-probable assumptions. Thus, research with fewer assumptions must use the non-parametric testing method. Consequently, a study population with ordinal value, clear outliers, and outcome detection must use the nonparametric test.
Appropriate measures of t-test, z-test and nonparametric test.
Public health researchers utilize differential statistics to evaluate the population size. By implication, two or more variables are computed based on the experimental design. Thus, public health researchers rely on inferential analysis of two or more variables (Rao, 2007). The variables could be nominal, ordinal or interval, thus, comparative analysis improves the research validity. The paper defined research hypothesis as a statement of assumption. However, hypothesis testing process describes the validity of the research statement. The steps for hypothesis testing include a defined null hypothesis, an alternative hypothesis, decision set, sample collection, analysis of data, accept or reject regions, and hypothesis validation or conclusion. The null hypothesis describes the reversed version of the researcher’s assumptions. For example, tooth decay among Hispanic black males requires a null hypothesis to validate the study. Consequently, the research provides a working hypothesis and an alternative. However, if the null hypothesis is rejected, the alternative hypothesis will validate the research statement.
The set analysis describes the researchers’ decisions. By implication, an assumed significance of 0.05 will establish the researcher’s confidence level for the hypothesis testing. Data collection must be carried out using random or observational methods. For example, the inclusion criterion for data collection in the tooth decay study was age, and ethnicity. Finally, analysis of data depends on the statistical technique. The test is computed using a z-test or the t-test. However, the test method depends on the researcher’s decision, the population, size, nominal distribution, and population variance. The researcher can accept or reject the hypothesis statement based on the threshold value. In accordance with the research rules, the researcher draws the conclusion of the study. An appropriate statistical test for the sample depends on the sample size and nominal distribution. However, statistical testing methods include comparison, confidence interval, and point estimates. With reference to the sample study, the t-test will establish the confidence interval of the sample population.
Reference
Rao, K. V. (2007). Biostatistics: A manual of statistical methods for use in health, nutrition and anthropology. India, New Delhi: Jaypee Brothers Publishers.
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