Statistics for Researchers

Solid understanding of descriptive statistics (mean, average, standard deviation, variance) and basic understanding of probability is required.

You may want to participate in preparation course: Statistics Level 1

This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines.

It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs.

In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...)

In the case of public courses, mixed examples are used.

Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion.

This course can be delivered as a blended course i.e. with homework and assignments.

Scientific Method, Probability & Statistics

• Very short history of statistics
• Why can be "confident" about the conclusions
• Probability and decision making

Preparation for research (deciding "what" and "how")

• The big picture: research is a part of a process with inputs and outputs
• Gathering data
• Questioners and measurement
• What to measure
• Observational Studies
• Design of Experiments
• Analysis of Data and Graphical Methods
• Research Skills and Techniques
• Research Management

Describing Bivariate Data

• Introduction to Bivariate Data
• Values of the Pearson Correlation
• Guessing Correlations Simulation
• Properties of Pearson's r
• Computing Pearson's r
• Restriction of Range Demo
• Variance Sum Law II
• Exercises

Probability

• Introduction
• Basic Concepts
• Conditional Probability Demo
• Gamblers Fallacy Simulation
• Birthday Demonstration
• Binomial Distribution
• Binomial Demonstration
• Base Rates
• Bayes' Theorem Demonstration
• Monty Hall Problem Demonstration
• Exercises

Normal Distributions

• Introduction
• History
• Areas of Normal Distributions
• Varieties of Normal Distribution Demo
• Standard Normal
• Normal Approximation to the Binomial
• Normal Approximation Demo
• Exercises

Sampling Distributions

• Introduction
• Basic Demo
• Sample Size Demo
• Central Limit Theorem Demo
• Sampling Distribution of the Mean
• Sampling Distribution of Difference Between Means
• Sampling Distribution of Pearson's r
• Sampling Distribution of a Proportion
• Exercises

Estimation

• Introduction
• Degrees of Freedom
• Characteristics of Estimators
• Bias and Variability Simulation
• Confidence Intervals
• Exercises

Logic of Hypothesis Testing

• Introduction
• Significance Testing
• Type I and Type II Errors
• One- and Two-Tailed Tests
• Interpreting Significant Results
• Interpreting Non-Significant Results
• Steps in Hypothesis Testing
• Significance Testing and Confidence Intervals
• Misconceptions
• Exercises

Testing Means

• Single Mean
• t Distribution Demo
• Difference between Two Means (Independent Groups)
• Robustness Simulation
• All Pairwise Comparisons Among Means
• Specific Comparisons
• Difference between Two Means (Correlated Pairs)
• Correlated t Simulation
• Specific Comparisons (Correlated Observations)
• Pairwise Comparisons (Correlated Observations)
• Exercises

Power

• Introduction
• Example Calculations
• Factors Affecting Power
• Exercises

Prediction

• Introduction to Simple Linear Regression
• Linear Fit Demo
• Partitioning Sums of Squares
• Standard Error of the Estimate
• Prediction Line Demo
• Inferential Statistics for b and r
• Exercises

ANOVA

• Introduction
• ANOVA Designs
• One-Factor ANOVA (Between-Subjects)
• One-Way Demo
• Multi-Factor ANOVA (Between-Subjects)
• Unequal Sample Sizes
• Tests Supplementing ANOVA
• Within-Subjects ANOVA
• Power of Within-Subjects Designs Demo
• Exercises

Chi Square

• Chi Square Distribution
• One-Way Tables
• Testing Distributions Demo
• Contingency Tables
• 2 x 2 Table Simulation
• Exercises

Case Studies

Analysis of selected case studies