Steps in testing a research hypothesis

An assertion about a population parameter developed for testing is known as a hypothesis. Testing the hypothesis involves some steps. The first step is stating the null hypothesis as well as the alternative hypothesis. The null hypothesis is the statement to be tested while the alternative is the statement to be accepted if the null hypothesis is false. The next step is to identify the sample distribution as well as specify the test statistic (Stephens, Buskirk, Hayward & Martinez Del Rio, 2005). The Z-test is used in most cases as the test statistic.

The next step involves stating the decision rules. This includes stating the rules or conditions under which the null hypothesis is to be accepted or rejected. A critical value, which separates the reject and non-reject regions, is determined (Stephens, Buskirk, Hayward & Martinez Del Rio, 2005). After setting the rules, the test statistic is then calculated from the data and the decision to accept or reject the hypothesis is made. Lastly, the results are then interpreted and a conclusion stated in the context of the original hypothesis.
Comparing the means of two or more groups

There are two principal methods used to compare the means of two or more groups. These are the t-test and analysis of variance (ANOVA). The t-test is generally used to compare the means of two groups while ANOVA is used in cases of more than two groups. The major assumption of the t-test is that the samples are randomly drawn from normally distributed populations. It can be conducted on one, paired or independent samples. One sample test compares the mean with a hypothesized value (Wilcox, 1990). The independent samples test compares the sample mean with the condition set by the null hypothesis.

The primary underlying assumption for ANOVA is normality and random selection. In addition, the samples should have an equal standard selection. ANOVA tries to determine if all the involved means are equal by analyzing the variances of all samples.
Calculating the correlation between two variables

The presence of correlation between two variables indicates that a change in one variable will most likely cause a change in the other variable. A high correlation indicates a strong relationship between the variables. To determine whether two variables are related, the correlation coefficient is calculated. The coefficient is between -1 and +1. If the coefficient is positive, this indicates a positive linear relationship between the variables. A negative coefficient indicates a negative linear relationship. A zero coefficient signifies no relationship exist between the variables (Meng, Rosenthal & Rubin, 1992).

 References

Meng, X. L., Rosenthal, R., & Rubin, D. B. (1992). Comparing correlated correlation coefficients. Psychological bulletin, 111(1), 172.

Stephens, P. A., Buskirk, S. W., Hayward, G. D., & Martinez Del Rio, C. (2005). Information theory and hypothesis testing: a call for pluralism. Journal of applied ecology, 42(1), 4-12.

Wilcox, R. R. (1990). Comparing the means of two independent groups. Biometrical Journal, 32(7), 771-780.

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