# SPSS Session 2: Hypothesis Testing and p-Values

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SPSS Session 2: Hypothesis Testing and p-Values

Learning Objectives Review Lectures 8 and 9
Understand and develop research hypotheses and know difference between them and the null hypothesis Define independent and dependent variables for a research hypothesis Define probability and describe it’s relationship to statistical significance

Review of Lecture 8 Defined and discussed the theory and rules of probability Calculated probability and created a probability distribution with example data Described the characteristics of a normal curve and interpreted a normal curve using example data

Review from Lecture 9 Defined research hypothesis, null hypothesis and statistically significance Discussed the basic requirements for testing the difference between two means Defined and described the difference between the alpha value and P value, and Type I and Type II errors

Research Hypotheses Hypotheses give a testable and potentially falsifiable prediction about the relationship between two variables. Designed to answer a research question of particular interest. For example, in our child protection study, parent or carer stress was predicted to be significantly associated with the quality of the family environment. This was a central hypothesis. Our research question was: Is parent or carer stress associated with the quality of the family environment?

Research and Null Hypotheses
RESEARCH HYPOTHESIS A proposed explanation for a phenomenon that can be tested There is a relationship between two measured variables A particular intervention makes a difference/has an effect NULL HYPOTHESIS The opposite position of the hypothesis (usually) There is no relationship between two measured variables The particular intervention does not make a difference/has no effect

Research and Null Hypotheses Examples
RESEARCH HYPOTHESIS Symbolized as “H1” Parent or carer stress will be significantly associated with the quality of the family environment. NULL HYPOTHESIS Symbolized as “H0” Parent or carer stress will not be significantly associated with the quality of the family environment.

Alternative Hypotheses
Alternative or rival hypotheses may offer another explain on why two variables may or may not be associated Alternative hypotheses are based on the information that you may not have collected or didn’t consider for every possible variable Other variables can: Be the actual cause Alter the relationship between the two variables It is important to read prior research literature before doing your research and data collection

Independent and Dependent Variables
Independent variables (IV) those variables of interest which are used to predict dependent variables (DV) Independent variables are also called “Predictors”. Dependent variables are also called “Outcomes”. That is IV explain variation in DV. For example, parent or carer stress (IV) was predicted to be significantly associated with the quality of the family environment (DV).

Probability Research and quantitative tests produce results in probabilistic Probability that the association found between an IV and DV occurred due to chance Can also be said that the association between the IV and DV was statistically significant, and therefore not due to chance

Statistical Significance
In order to determine if something is statistically significant, you must establish a level of significance (represented by the Greek letter α [alpha]). α = the level of probability where the null hypothesis can be rejected with confidence and the research hypothesis accepted with confidence A common level of significance α = .05

Statistical Significance
In statistical analyses, we find the p-value of the association between two variables (IV and DV). If the p-value is less than our α = .05 level of significance, when we reject our null hypothesis and accept our research hypothesis. If the p-value is greater than our α = .05 level of significance, when we say that we retain or fail to reject our null hypothesis.