Click on the date for more information about each lecture
Detailed version of the full syllabus is available here
Date  Topic  Reading 

1/7  What is statistics? overview of the course Learning Objectives: After this lecture, you should be able to: * Describe the central goals and fundamental concepts of statistics. * Describe the difference between experimental and observational research with regard to what can be inferred about causality * Explain how randomization provides the ability to make inferences about causation. 
Chapter 1 
1/9  No class (sick day)  content will be made up on 1/23 

1/11  R Lab: Basics Learning Objectives: After this lecture, you should be able to: * Interact with an RMarkdown notebook in RStudio * Describe the difference between a variable and a function * Create a vector, matrix, or data frame and access its elements * Load a data file into a data frame and plots its contents Links: * For additional practice with R, check out the free courses provided by Datacamp. In particular, their Introduction to R provides a nice basic overview of working in R. 
Install RStudio (instructions) Complete Intro to basics section of DataCamp Introduction to R (https://campus.datacamp.com/courses/freeintroductiontor) 
1/14  Probability Learning Objectives: After this lecture, you should be able to: * Describe the sample space for a selected random experiment. * Compute relative frequency and empirical probability for a given set of events * Compute probabilities of single events, complementary events, and the unions and intersections of collections of events. * Describe the law of large numbers. Links: * R notebook for lecture 
Chapter 3 (Sections 3.13.3) 
1/16  Probability, cont. Learning Objectives: After this lecture, you should be able to: * Describe the difference between a probability and a conditional probability * Describe the concept of statistical independence * Use Bayes’ theorem to compute the inverse conditional probability. 
Chapter 3 (Sections 3.43.10) 
1/18  R lab: probability Learning Objectives: After this lecture, you should be able to: * Intro to Rmarkdown notebooks * Compute probabilities of combinations of events * Compute an empirical probability distribution * Describe the different functions available for the normal distribution, and their usage 
http://www.cyclismo.org/tutorial/R/probability.html 
1/21  no class, MLK day  ** 
1/23  Working with data (makeup for session 2) Learning Objectives: After this lecture, you should be able to: * Distinguish between different types of variables (quantitative/qualitative, discrete/continuous, scales of measurement) * Describe the concept of measurement error * Distinguish between the concepts of reliability and validity and apply each concept to a particular dataset 
Chapter 2 
1/25  Summarizing data Learning Objectives: After this lecture, you should be able to: * Compute absolute, relative, and cumulative frequency distributions for a given dataset * Generate a graphical representation of frequency distributions * Describe the difference between a normal and a longtailed distribution, and describe the situations that give rise to each Links: * R Notebook for lecture * Social network data 
Chapter 4 
1/28  R lab: Data wrangling and visualization Learning Objectives: After this lecture, you should be able to: * Describe the concept of tidy data * Load a data file and prepare it for analysis * Plot summary graphs using ggplot 
Introduction and Section 2.1 of https://garrettgman.github.io/tidying/ Sections 3.13.6 of http://r4ds.had.co.nz/datavisualisation.html 
1/30  Fitting models (central tendency) Learning Objectives: After this lecture, you should be able to: * Describe the basic equation for statistical models (outcome=model + error) * Describe different measures of central tendency and dispersion, how they are computed, and how to determine which is most appropriate in any given circumstance. Links: * R Notebook for lecture 
Chapter 5 
2/1  Visualizing data Learning Objectives: After this lecture, you should be able to: * Describe the principles that distinguish between good and bad graphs, and use them to identify good versus bad graphs. 
Chapter 6 
2/4  Sampling Learning Objectives: After this lecture, you should be able to: * Distinguish between a population and a sample, and between population parameters and statistics * Describe the concepts of sampling error and sampling distribution * Describe how the Central Limit Theorem determines the nature of the sampling distribution of the mean Links: * R notebook for lecture 
Chapter 7 
2/6  Resampling and simulation Learning Objectives: After this lecture, you should be able to: * Describe the statistical concept of a random number * Describe the concept of Monte Carlo simulation * Describe the concept of the bootstrap and use it to estimate the sampling distribution of a statistic Links: * R notebook for lecture 
Chapter 8 Watch video: https://www.youtube.com/watch?v=tClZGWlRLoE 
2/8  R Lab: Simulation and resampling Learning Objectives: After this lecture, you should be able to: * Demostrate the ability to implement a Monte Carlo simulation in R 
Watch video: https://www.youtube.com/watch?v=tvv4IA8PEzw&t=482s (note that the video talks about several concepts that we have not learned about in class  just go with it and focus on the Rrelated material) 
2/11  Hypothesis testing Learning Objectives: After this lecture, you should be able to: * Identify the components of a hypothesis test, including the parameter of interest, the null and alternative hypotheses, and the test statistic. * Describe the proper interpretations of a pvalue and a confidence interval as well as common misinterpretations * Distinguish between the two types of error in hypothesis testing, and the factors that determine them. Links: * R notebook for lecture: https://rawgit.com/psych10/psych10/master/notebooks/Session16HypothesisTesting/Session16HypothesisTesting.html 
Chapter 9 
2/13  Hypothesis testing, cont. Learning Objectives: After this lecture, you should be able to: * Describe how resampling can be used to compute a pvalue. * Define the concept of statistical power, and compute statistical power for a given statistical test. * Describe the main criticisms of null hypothesis statistical testing 
Chapter 9, cont. 
2/15  Confidence intervals and effect sizes Learning Objectives: After this lecture, you should be able to: * Describe the proper interpretation of a confidence interval, and compute a confidence interval for the mean of a given dataset. * Define the concept of effect size, and compute the effect size for a given test. 
Chapter 10 
2/18  No class, Presidents Day 

2/20  Bayesian analysis Learning Objectives: After this lecture, you should be able to: 
Chapter 11 https://www.youtube.com/watch?v=BcvLAwJRss 
2/22  Bayesian analysis, cont. Learning Objectives: After this lecture, you should be able to: 

2/25  Bayesian analysis, cont. 2 

2/27  Modeling categorical relationships Learning Objectives: After this lecture, you should be able to: * Describe the concept of a contingency table for categorical data. * Describe the concept of the chisquared test for association and compute it for a given contingency table. 
Chapter 12 
3/1  Modeling continuous relationships (RP Gone  need guest lecturer) Learning Objectives: After this lecture, you should be able to: * Describe the concept of the correlation coefficient and its interpretation and compute it for a bivariate dataset * Describe the potential causal influences that can give rise to a correlation. Links: * Spurious Correlations 
Chapter 13 
3/4  The general linear model Learning Objectives: After this lecture, you should be able to: * Describe the concept of linear regression and apply it to a bivariate dataset * Describe the concept of the general linear model and provide examples of its application 
Chapter 14 
3/6  Comparing means Learning Objectives: After this lecture, you should be able to: * Determine whether a onesample ttest or twosample ttest is appropriate for a given hypothesis. * Compute a onesample and twosample ttest on relevant datasets, and compute the effect size and confidence intervals associated with each of these tests. 
Chapter 15 
3/8  Statistical Inference R lab Learning Objectives: After this lecture, you should be able to: * Demonstrate the ability to apply statistical models to real data in R 

3/11  Statistical modeling: Practical examples Learning Objectives: After this lecture, you should be able to: * Describe how to determine what kind of model to apply to a dataset 
Chapter 16 
3/13  Doing reproducible research Learning Objectives: After this lecture, you should be able to: * Describe the concept of Phacking and its effects on scientific practice * Describe the concept of positive predictive value and its relation to statstical power Links: * Fivethirtyeight Phacking demo 
Chapter 17 Simmons et al (available on Canvas) https://www.buzzfeed.com/stephaniemlee/brianwansinkcornellphacking?utm_term=.gtAVwLX2GM#.fep9L6pw78 
3/15  Doing reproducible research, cont 

3/21  Final meeting with project presentations, 8:3011:30am 