Learning Outcomes:

• Understand the logic of hypothesis testing;

• Be aware of the errors in hypothesis testing;

• Understand and apply the concept of hypothesis testing;

• Know how to report the results of the statistical test.

CHAPTER 4

 HYPOTHESIS TESTING AND STATISTICAL SIGNIFICANCE

Hypothesis - A prediction of how our variables might be related to each other or how they may affect each other. 

Inferential Statistics - This is where we are already capable of concluding whether our variables are related to one another or not, or whether our variables affect one another. - Keyword: CONCLUSION

Probability Value

• P-value 

The likelihood of us obtaining our pattern of results due to sampling error if there is no relationship between our variables

Null Hypothesis

• Always states that there is no effect on the underlying population

Research Hypothesis - 

• is the prediction of how two variables might be related to each other. 

Logic of Null Hypothesis Testing 

• to formulate a hypothesis

• to measure the variables involved and examine the relationship between them

P-value 

• determines whether there is evidence to reject the null hypothesis

*95% - probability that the null hypothesis is true

*5% - statistical significance level / α

THE SIGNIFICANCE LEVEL

• Alpha (α) -the criterion for statistical significance that we set for our analyses.

-if the probability of…is less than 5%, then the findings are said to be significant.

-if the probability of… is greater than 5%, then the findings are said to be non-significant.

PARAMETRIC TESTS 

•  uses/considers the parameters (sample mean, the population mean, etc.) of the population 

• data must be on an interval scale (not nominal nor ordinal)

• data must be normally distributed

• homogeneity of variance

• there should be no extreme scores/outliers

NON-PARAMETRIC TESTS

• opposite of parametric tests