Problem Formulation and Techniques

Hypothesis formulation and testing involves creating a testable statement, called a "hypothesis," about a predicted relationship between variables in a research study, and then collecting data to either support or reject that statement, with the goal of drawing conclusions based on empirical evidence; research assumptions are underlying beliefs considered true within the study that inform the hypothesis but are not directly tested themselves. 

Statistical hypothesis testing requires several assumptions. These assumptions include considerations of the level of measurement of the variable, the method of sampling, the shape of the population distribution, and the sample size. The specific assumptions may vary, depending on the test or the conditions of testing.

Hypothesis Formation: In conducting hypothesis-driven research (especially in quantitative studies), you will formulate a hypothesis, which is a testable prediction about the relationship between variables.

• Example: “Online learning will improve high school students' performance in mathematics compared to traditional classroom learning.”

Key points about hypothesis formulation and testing:

• Hypothesis:

A proposed explanation for a phenomenon that can be tested through research, usually stated as an "if-then" statement. 

• Null Hypothesis (H0):

A statement that there is no significant effect or relationship between variables, essentially the default assumption that is tested against. 

• Alternative Hypothesis (Ha):

The statement that the researcher wants to prove, proposing a specific relationship or effect between variables.