, we will apply strategies for hypothesis testing, determine when to use one-or two-tailed Z test and interpret the results of a one- and two-sample t test. Respond to the following in a minimum of 175 words: Define the following: Research hypothesis Null hypothesis Hypothesis Hypothesis testing Hypothesis testing process Dependent variable Independent variable ) conventional levels of significance Statistically significant One-tailed test Two-tailed test Theory Cutoff sample score Directional hypothesis

In hypothesis testing, several key concepts and terms are used to define and guide the process. Understanding these terms is essential for effectively conducting hypothesis testing and interpreting the results.

Firstly, let’s define the research hypothesis. The research hypothesis is a statement that specifies the anticipated relationship between variables or the expected outcome of a study. It typically represents the proposition that the researcher aims to prove through their investigation. The research hypothesis is formulated based on existing theory, previous research, or observations.

The null hypothesis, on the other hand, is the opposite of the research hypothesis. It represents the proposition that there is no significant difference or relationship between variables being studied. The null hypothesis assumes that any observed differences or relationships are due to chance or sampling error. It is denoted by H0.

Hypothesis testing is the process of assessing the validity of a research hypothesis by collecting and analyzing data. It involves setting up competing hypotheses (the research and null hypotheses) and making a decision based on the evidence gathered.

The hypothesis testing process generally follows a set of steps. First, the researcher states the research hypothesis and the null hypothesis. Then, data is collected and analyzed using statistical methods to evaluate the likelihood of obtaining the observed results under the null hypothesis. The analysis generates a test statistic, which is then compared to a predetermined significance level to determine if the results are statistically significant.

The dependent variable refers to the variable being measured or studied in an experiment. It is the outcome variable that researchers are interested in explaining or predicting. The independent variable, on the other hand, is the variable that researchers manipulate or control in an experiment to assess its impact on the dependent variable.

Conventional levels of significance are predetermined thresholds used to decide whether the results of a statistical test are statistically significant. These levels (commonly denoted as α) typically range from 0.01 to 0.05. If the p-value (a measure of the strength of evidence against the null hypothesis) calculated from the data falls below the chosen level of significance, the null hypothesis is rejected in favor of the research hypothesis.

A statistically significant result is one that is unlikely to have occurred by chance alone. When the observed data provides strong evidence against the null hypothesis, we conclude that there is a significant relationship between variables or a significant difference in means.

In hypothesis testing, we can use either a one-tailed test or a two-tailed test. A one-tailed test is used when we have a specific directional research hypothesis. It examines whether the test statistic falls in one particular tail of the distribution. On the other hand, a two-tailed test is used when the research hypothesis is non-directional, and we want to examine if the test statistic falls in either tail of the distribution.

Theory, in the context of hypothesis testing, refers to an organized and coherent set of principles or statements that explain or predict a phenomenon. A theory is often based on previous research, observations, and established knowledge in a particular field. It forms the foundation for generating research hypotheses and guiding scientific inquiry.

The cutoff sample score or critical value is a threshold against which the test statistic is compared to make a decision about the null hypothesis. If the test statistic exceeds the critical value, the null hypothesis is rejected, providing evidence in favor of the research hypothesis. The critical value is determined based on the chosen level of significance and the distribution of the test statistic.

Finally, a directional hypothesis is a specific form of research hypothesis that predicts the direction of the relationship or difference between variables. It specifies whether the variables are expected to increase or decrease in a particular direction. In contrast, a non-directional or two-tailed hypothesis does not make any specific predictions about the direction of the relationship or difference.