Course aims:

- make the task of learning statistics as easy and painless as possible. - explain how statistical methods can be classified using the same categories that are used to group data structures and research methods
- understanding of the basic concepts underlying a statistical formula - introducing statistical technique that uses the mean and the standard deviation to transform each score (X value) into a z-score, or a standard score
- combine the concepts of z-scores, probability, and the distribution of sample means to create a new statistical procedure known as a hypothesis test

Course Entry Requirements:

N/A

Course contents:

1. Data structures, reasearch methods and statistical structures 2. Frequency distribution 3.Central tendancy 4. Variability 5. z-Scores: Location of scores and standardized distribution 6. Hypothesis testing 7. Using t statistics for inferences about population means and mean differences

Teaching methods:

Lecture, discussion, exemplification.

Learning outcomes:

After following these course students will be able to: • define and operationalize the measurement object and choose the appropriate techniques • know and apply the steps of selection and analysis of measurable indicators • know and apply algorithms to construct statistical indicators • know and apply the techniques and methods of statistical analysis, estimation and interpretation of data, evaluation criteria and procedures for measuring instruments; • select and apply the most appropriate evaluation methods • know and apply the techniques fot t test

Learning outcomes verification and assessment criteria:

Final evaluation – 50%; Seminar activities – 50%.

Recommended reading:

Frederick J Gravetter Larry B. Wallnau, Statistics for the Behavioral Sciences, Wadsworth, 2013,
G. Keppel, Design and analysis: A researcher’s handbook, Prentice-Hall, 1973,
David C. Howell, Fundamental Statistics for the Behavioral Sciences, Wadsworth Publishing, 2010,