## Introduction

The Psychological research question for my analysis is: Does student anxiety for an exam increase the study hours?

Null hypothesis (H_{0}) states that student anxiety for an exam has no significant effect on the number of study hours.

Alternative hypothesis (H_{1}) states that student anxiety for an exam has a significant effect on the number of study hours.

Correlation does not depend on any statistical variables. The aforementioned statement makes it unique in giving unbiased comparison between variable units (Anselin & Rey, 1997).

To calculate the correlation between student anxiety scores and number of study hours, we use the formula below: Let X represent student scores and Y represent number of study hours.

Where N = 10

First, we calculate the means of the student anxiety scores and the study hours respectively.

Given that alpha is 0.05%, the effect size is 0.3. According to this, the effect size is greater than the critical value thus; we accept the null hypothesis that student anxiety for an exam has no significant effect on the number of study hours.

## How to conduct a t and ANOVA tests

The t and ANOVA tests examine the ratio of the data groups analyzed in relation to each other. They allow researchers to differentiate a clear signal of statistical change and an appearance of similarity that otherwise does not count as significant change. A huge variability of data increases the difficulty of noticing statistical differences, whereas a mean difference serves as a signal for a statistical significance in the two groups of data. Researchers use two-tailed tests to analyze a non-directional hypothesis. Under a non-directional hypothesis testing, results are interesting in both directions of the mean. The null hypothesis is a specific value and the alternative hypothesis only states that the results will be different. ANOVA uses the effect size of F test to draw results by chance. To clarify whether this is true, the researcher looks up the F-test value in a table of significance specifically to check whether the ratio is larger than a given value for chance (Anselin & Rey, 1997).

The this risk level, also known as alpha level, is set to 0.05 implying that chance would account for five out of one hundred tests. A researcher will also need the degrees of freedom, which is the sum of the number of data values in all the groups after a subtraction (Vicky, 2009). The three values namely, the alpha value, the degrees of freedom value and the F-test value form a complete set for looking up in a standard table of significance.

## Behavioral research situation

The research data given are essential in choosing the tests of significance to be applied in a research study when working out a solution. Pearson coefficient and chi square research study works well with discrete counted data (Vicky, 2009). It is applicable in a situation where there are distinct categories i.e. male and female or in a psychological question which seeks to look into artificial attributes like tall and short.

## References

Anselin, L. & Rey, S. (1997). Introduction to special issue on spatial econometrics. *International Regional Science Review, 20*(1-2).

Vicky R. N. (2009). *T-tests and One-Way ANOVA*. Web.