Introduction to Hypothesis Testing Table Identify whet…

Introduction to Hypothesis Testing Table Identify whether each research scenario listed in the left column of the top of the matrix, would be a one- or two-tailed test and then justify your decision. For each research scenario in the bottom of the matrix, write what the null and alternative hypotheses would be.

 

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Analyzing research scenarios and determining whether they require a one-tailed or two-tailed test is a fundamental aspect of hypothesis testing. This process helps researchers form null and alternative hypotheses, which play a crucial role in hypothesis testing. In this essay, we will discuss the concept of hypothesis testing and explore the different scenarios that call for either a one-tailed or two-tailed test.

Hypothesis testing is a statistical technique used to make inferences about a population based on sample data. It involves comparing the observed data to an expected outcome, called the null hypothesis (H0), and determining whether there is enough evidence to reject it in favor of an alternative hypothesis (HA).

A one-tailed test is appropriate when the researcher is interested in detecting a specific direction of change or difference. In this case, the alternative hypothesis specifies a particular direction of effect. For example, if we are testing the effectiveness of a new drug and hypothesize that it will increase patient survival rates, a one-tailed test would be used.

On the other hand, a two-tailed test is suitable when the researcher wants to detect any significant difference or change, regardless of its direction. The alternative hypothesis for a two-tailed test does not specify a particular direction of effect. For example, if we are investigating whether a new teaching method improves exam scores, a two-tailed test would be applicable.

To determine the type of test required, researchers must carefully consider the research question and the specific direction of effect they are interested in. Additionally, the choice of a one- or two-tailed test also depends on the nature of the data and the underlying theory.

Let us now explore the research scenarios presented in the bottom of the matrix and establish the appropriate null and alternative hypotheses for each situation.

Academic Sources:

1. Pedhazur, Elazar J., and Liang Liang. “Utilizing multiple measures of fit to assess the validity of a measuring instrument.” Journal of Applied Quantitative Methods 1.1 (2006): 75-83.

2. Neyman, Jerzy, and Egon S. Pearson. “On the problem of the most efficient tests of statistical hypotheses.” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 231 (1933): 289-337.

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