Statistical significance mostly depends on three main factors size of the sample being studied, variability and differences which occur between the means. Null and alternative hypotheses are used to predict possible outcome of a study. For example a researcher would wish to compare the attitudes of students towards school by comparing fourth graders and sixth graders. The researcher would obtain the mean for both groups and then the population mean of both groups shall be estimated and compared so as to determine probability of a possibility that the mean of each group is different (McMillan & Schumacher 2006).
T-test is used to differentiate between two data samples by use of equal or unequal variance or independence and dependence samples. The independent samples are for subjects which do not depend on each other. Research of the students attitude in fourth graders and sixth graders would use independence t-testing because they are two groups which are independent (McMillan & Schumacher 2006). Dependent t-testing would be applicable when tests are done more than once in the same group or the two groups interrelate with each other. Analysis of Variance (ANOVA) test differences between two sample data however the dependent variable must be ratio data or interval.
Chi-square is commonly used to study relationships which occur between various levels of multiple or one variable and it is used for nominal level data. It is mostly used by researchers to examine frequency of occurrences. For example it would be used to study how often fourth graders and sixth graders are involved in cheating in exams.
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