Bivariate Analaysis

Bivariate Analysis
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Bivariate Analysis
The type of data analysis used on a specific population will depend on the number of variables. With the bivariate analysis conducted, a number of key explanations can be observed. For the data presented, the test conducted on the data was a paired t-squared test. This is because there are two samples in which observations in each sample can be compared to one another (Kim, 2015). In this case, samples under study are pretest life skills and posttest life skills. The analysis conducted was a non-parametric statistic. There were no assumptions made on the hypothesis and so the statistics used qualify as being non-parametric.
The first table represents data from two sample groups which are an individual’s life skills pretest and an individual’s life skills posttest. Each of the samples has a total of 185 participants. The mean and standard deviation for the life skills pretest are 43.44 and 8.958 respectively while the mean and standard deviation for the life skills posttest are 64.18 and 14.934 respectively. From the analysis, hypotheses can be formulated. The null hypothesis will be individuals with pretest life skills will not have a difficult time in the future while the research hypothesis will be individuals with posttest life skills will have a difficult time in the future.
Given the data from the table, the p-value for the experiment was determined to be 0.00. Looking at the value, it is lower than the significance level set which is 5% or 0.

05. The results are therefore statistically significant. With the p-value in mind, the null hypothesis should be rejected. The rejection of the null hypothesis is determined by the p-value. If the value was above 0.05, then the null hypothesis could be accepted as it could have been above the significance level. With the results in mind, the results support the research hypothesis.
References
Kim, T.K. (2015). T test as a parametric statistic. Korean Journal of Anesthesiology, 68(6), 540-546.