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Educational statistics

Course Description: Topics to be Covered: Measures of Central Tendency: Arithmetic Mean, Median, Mode, and the Relationship Between Measures of Central Tendency. Measures of Dispersion: Range, Standard Deviation, Variance, Interquartile Range. Data Exploration: Normal Curve, Skewness, Kurtosis. Some Parametric Statistical Methods: • t-Test: Conditions for Use, Different Cases, Interpretation of Results and Effect Size. • Analysis of Variance (ANOVA): Concept/When to Use, How to Use, Interpretation of Results, Effect Size. • Correlation Coefficients: Pearson Correlation Coefficient, When to Use, How to Use, Interpretation of Results, Effect Size. Some Non-Parametric Statistical Methods: • Wilcoxon Test: To Detect the Significance of Differences Between Ranks of Scores of Related Groups. • Mann-Whitney Test: To Detect the Significance of Differences Between Ranks of Scores of Independent Groups. • Differences Between Two Proportions: Independent, Dependent. • Chi-Square Test. • Correlation Coefficients: Spearman Rank Correlation Coefficient, Phi Coefficient.

Credits: 2

Objectives of the course :

This course aims to help the student to:
• Provides a correct definition of statistical concepts: sample, population, parametric statistics, descriptive statistics, inferential statistics.
• Recognizes when to use prior statistical methods.
• Lists possible statistical errors.
Able to classify data and present it graphically and computationally.
• Recognize the relationship between measures of central tendency: mean, median, mode.
• Differentiate between measures of dispersion: range, standard deviation, interquartile range.
• Recognizes the meaning of percentage distribution and how to ensure the normality of score distribution.
• Capable of applying some parametric statistical methods such as: t-test, correlation coefficients, analysis of variance, regression analysis.
• Lists conditions for using accessible parametric methods and how to calculate effect size using them.
• Ability to apply some non-parametric statistical methods such as: Mann-Whitney test, Wilcoxon test, differences between proportions, Chi-square test, non-parametric correlation coefficients.
• Lists the conditions for using non-parametric methods and how to interpret the results that can be obtained.

Course outputs :

• Understand the concept of: sample, population, parametric statistics, descriptive statistics, inferential statistics, parametric and non-parametric statistical methods.
• Recognizes when to use and statistical decisions.
• Lists possible statistical errors.
• Recognizes measures of central tendency and how to calculate them, and measures of dispersion and how to calculate them.
• Recognize some parametric statistical methods such as: the t-test and correlation coefficients.
• Certain non-parametric statistical methods are recognized, such as: differences between proportions, the chi-square test, and correlation coefficients.
• Recognizes probabilities, their meanings, and their mathematical properties.
• The learner will be able to: Understand statistical concepts, frequency distributions and graphical representations, measures of central tendency, measures of dispersion, data exploration, and become familiar with some parametric and non-parametric statistical methods, and correlation coefficients.

Additional information:

Last updated: 2026-06-03