# Answer the following in 60 words or more1.What is the differ…

Answer the following in 60 words or more 1. What is the difference between a one-tailed and a two-tailed test of significance?  Under what circumstances would each be used? 2. Explain the process of hypothesis testing in evaluating possible solutions to a psychological research problem. 3. Formulate a null and alternative hypothesis 4. What is the five-step process for hypothesis testing?  Explain each step with a personal example of something that you experienced.

1. The difference between a one-tailed and a two-tailed test of significance lies in the directionality of the hypothesis being tested. In a one-tailed test, the hypothesis specifies the direction of the effect, either positive or negative. In a two-tailed test, the hypothesis does not specify the direction and seeks to determine if there is a significant difference in either direction. One-tailed tests are used when previous research or theory strongly supports a specific direction of effect, whereas two-tailed tests are used when there is no strong theoretical basis for predicting a specific direction.

2. Hypothesis testing is a statistical procedure used to evaluate the plausibility of a research hypothesis and make inferences about the population based on sample data. The process involves several steps. First, a research problem is identified and a research hypothesis is formulated. Then, a suitable statistical test is selected based on the characteristics of the data and the research question. Next, data is collected and analyzed using the selected statistical test. The test statistic is calculated, and its probability of occurring by chance is determined. Finally, a decision is made regarding the acceptance or rejection of the null hypothesis, based on the level of statistical significance chosen. This process helps researchers assess the likelihood that their observed results are due to chance alone and guides them in drawing meaningful conclusions.

3. In hypothesis testing, the null hypothesis (H0) represents the assumption of no effect or no difference in a population parameter, while the alternative hypothesis (HA) represents the hypothesis that contradicts the null hypothesis and suggests the presence of an effect or a difference. Formulating a null and alternative hypothesis requires carefully defining the research question and making predictions about the population parameter. For example, if a researcher wants to investigate whether a new treatment is more effective than a standard treatment for reducing symptoms of depression, the null hypothesis would state that there is no difference in symptom reduction between the new treatment and the standard treatment, whereas the alternative hypothesis would state that there is a difference, with the new treatment leading to greater symptom reduction.

4. The five-step process for hypothesis testing includes: (1) stating the research hypothesis and the null hypothesis, (2) selecting an appropriate statistical test, (3) collecting data and computing the test statistic, (4) determining the probability of obtaining the observed results assuming the null hypothesis is true, and (5) making a decision to either reject or fail to reject the null hypothesis. Each step plays a crucial role in generating meaningful results.

For example, suppose a researcher wants to test the hypothesis that there is a significant difference in mean exam scores between male and female students at a particular university. First, the researcher would state the research hypothesis (Ha: There is a significant difference in mean exam scores between male and female students) and the null hypothesis (Ho: There is no significant difference in mean exam scores between male and female students). Then, the researcher would select an appropriate statistical test, such as an independent samples t-test. Next, the researcher would collect the data by randomly sampling male and female students’ exam scores and compute the test statistic (e.g., t-value). The fourth step involves determining the probability of obtaining the observed results assuming the null hypothesis is true, which is done by calculating the p-value. Finally, the researcher makes a decision to either reject or fail to reject the null hypothesis based on the predetermined significance level (e.g., p < 0.05).