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Hypotheses are predictions that specify the relationships among the variables. The role of hypotheses in scientific research is to provide explanations for certain phenomena and to guide the investigation of related others. The development of scientific knowledge hinges ultimately upon the results from hypothesis testing. Formalized hypotheses consist of two types of variables: the independent and dependent variables. The former is the cause and the latter is the outcome. A good and well-worded hypothesis should (1) indicate the specific relationship between the dependent and independent variables to be examined; (2) suggest the nature of the relationship; and (3) imply the nature of the research design.
Hypotheses, which are derived directly from a theory or theories, have to be testable. The hypothesis-testing process generally involves three steps. The first step is to formulate two hypothesis statements: a null hypothesis (often symbolized as H0) that predicts no relationship between the variables in the population (e.g., H0: Social class is unrelated to deviant behavior) and an alternative hypothesis (H1) that predicts a relationship between the variables (e.g., H1: Social class is related to deviant behavior). The null hypothesis should be mutually exclusive of the alternative hypothesis, meaning that there is no overlap between the two hypotheses. They are also exhaustive, representing all possible outcomes in reality. If the null hypothesis is not correct or rejected, then the alternative hypothesis may be correct or accepted.
The second step is to select the level of significance. In order to decide whether to reject or fail to reject the null hypothesis, researchers must select a significance level (i.e., the a level) for the null hypothesis, which is typically at 0.05 or 0.01. If the alternative hypothesis specifies a direction of the relationship between the variables (e.g., H1: Social class is negatively related to deviant behavior), then the test is called a one-tailed or directional hypothesis test of significance, which looks for either the increase or decrease of the dependent variable. If the alternative hypothesis does not specify a direction of the relationship (H1: Social class is related to deviant behavior), then the test is called a two-tailed or non-directional hypothesis test of significance, which examines any change in the dependent variable.
A final step is to calculate the value of the test statistic and compare the statistic to a critical value obtained from distribution tables (e.g., distribution of t or chi square or F) based upon the a level. If the test statistic falls beyond the critical value, then the null hypothesis is rejected and the finding is significant (e.g., People with high and low social class differ significantly in their deviant behavior). If the test statistic does not exceed the critical value, then the null hypothesis cannot be rejected and the finding is not significant (e.g., People with high and low social class do not differ significantly in their deviant behavior), meaning that the difference in deviant behavior between people with high and low social class only occurs by random chance.
- Gravetter, F. & Wallnau, L. (2004) Statistics for the Behavioral Sciences. Wadsworth, Belmont, CA.
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