video summary

date

topic

reading

learning goals

1/7

Overview: What is statistics?

Chapter 1, ST21

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.

1/9

Working with data

Chapter 2, ST21

Distinguish between different types of variables (quantitative/qualitative, discrete/continuous)
Describe the concept of measurement error
Distinguish between the concepts of reliability and validity and apply each concept to a particular dataset

SECTION

R Lab: Introduction to R

Chapter 3, ST21

Interact with an RMarkdown notebook in RStudio
Use R/RStudio as a calculator
Define different kinds of variables in R

1/14

Summarizing data

Chapter 4, ST21

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 long-tailed distribution, and describe the situations that give rise to each

1/16

Visualizing data

Chapter 6, ST21

Describe the principles that distinguish between good and bad graphs, and use them to identify good versus bad graphs.

R Lab: Representing data

Chapter 5, ST21

How to store vectors as variables
Basic operations with vectors
Create a dataframe
Load data from an R package and view the data
Plot summary graphs using ggplot

1/21

Fitting models: central tendency

Chapter 8, ST21

Describe the basic equation for statistical models (outcome=model + error)
Describe different measures of central tendency, how they are computed, and which are appropriate under what circumstance.

1/23

Fitting models: variability

Describe different measures of dispersion, how they are computed, and how to determine which is most appropriate in any given circumstance. Describe and compute z-scores.

R Lab: Visualizing and summarizing data

Chapter 7, ST21

Understand advantages of “tidy” data
Visualize raw data as histograms and scatterplots

1/28

Probability: basic rules, everyday randomness

Chapter 10, ST21

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.
Learn about common cognitive illusions when thinking about probabilities

1/30

Probability: conditioning and independence

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.

R Lab: Descriptive statistics (central tendency, dispersion)

Chapter 9, ST21

Apply filtering to a dataframe
Compute standard measures of central tendency and dispersion
Apply z-score normalization to data

2/4

Sampling: sampling error and the Central Limit Theorem

Chapter 12, ST21

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

2/6

Sampling: Monte Carlo simulation and bootstrapping

Chapter 14, ST21

Learn about role of Monte Carlo simulation in statistics
Learn about resampling techniques to estimate the sampling distribution

R Lab: Probability

Chapter 11, ST21

Gain additional intuition for probability concepts by conducting simulations in R

2/11

Hypothesis testing: comparing means across two groups

Chapter 16 & 28, ST21

Determine whether a one-sample t-test or two-sample t-test is appropriate for a given hypothesis.
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 p-value as well as common misinterpretations
Distinguish between the two types of error in hypothesis testing, and the factors that determine them.

2/13

Hypothesis testing: comparing means across three or more groups; confidence intervals; effect size

Chapter 28, ST21

Compute a one-sample, two-sample t-test, and ANOVA on relevant datasets, and compute the effect size and confidence intervals associated with each of these tests.

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.

Learning how to read R output

R Lab: Sampling

Chapter 13 & 15, ST21

Gain intuition for Central Limit Theorem by conducting a simulation
Compute standard error of the mean for different sample sizes
Construct 95% confidence intervals

2/18

Resampling; statistical power; limitations of NHST

Describe how resampling can be used to compute a p-value.
Define the concept of statistical power, and compute statistical power for a given statistical test.
Describe the main criticisms of null hypothesis statistical testing

2/20

Quantifying effects: confidence intervals and effect size

Chapter 18, ST21

R Lab: Hypothesis testing

Chapter 17 & 19, ST21

Compute and interpret measures of effect size
Conduct t-test for difference in means and interpret results

2/25

Modeling continuous relationships (continuous outcome; continuous predictor)

Chapter 24, ST21

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.

2/27

Modeling categorical relationship (categorical outcome; categorical predictor)

Chapter 22, ST21

Describe the concept of a contingency table for categorical data.
Describe the concept of the chi-squared test for association and compute it for a given contingency table.

R Lab: ANOVA and correlation

Chapter 25, ST21

Conduct ANOVA for difference in means and interpret results
Compute Pearson correlation and gain intuition for what it means

3/3

General Linear Model: how different types of modeling are related to each other

Chapter 26, ST21

Understand t-tests, ANOVA, regression as variations of the General Linear Model

3/5

Experimental Research: replication/reproducibility; meta-analysis; challenges to cumulative science

Chapter 32, ST21

Describe the concept of P-hacking and its effects on scientific practice
Describe the concept of positive predictive value and its relation to statstical power

R Lab: Linear Regression

Chapter 27, ST21

Fit a linear regression model to data and interpret the results

3/10

Observational Research: strategies for modeling real-world data

Describe how to determine what kind of model to apply to a dataset

3/12

People’s Choice

R Lab: Final Project Workshop

Chapter 30, ST21

Demonstrate the ability to apply statistical models to real data in R

date

topic

reading

learning goals

1/7

Overview: What is statistics?

Chapter 1, ST21

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.

1/9

Working with data

Chapter 2, ST21

Distinguish between different types of variables (quantitative/qualitative, discrete/continuous)
Describe the concept of measurement error
Distinguish between the concepts of reliability and validity and apply each concept to a particular dataset

SECTION

R Lab: Introduction to R

Chapter 3, ST21

Interact with an RMarkdown notebook in RStudio
Use R/RStudio as a calculator
Define different kinds of variables in R

1/14

Summarizing data

Chapter 4, ST21

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 long-tailed distribution, and describe the situations that give rise to each

1/16

Visualizing data

Chapter 6, ST21

Describe the principles that distinguish between good and bad graphs, and use them to identify good versus bad graphs.

R Lab: Representing data

Chapter 5, ST21

How to store vectors as variables
Basic operations with vectors
Create a dataframe
Load data from an R package and view the data
Plot summary graphs using ggplot

1/21

Fitting models: central tendency

Chapter 8, ST21

Describe the basic equation for statistical models (outcome=model + error)
Describe different measures of central tendency, how they are computed, and which are appropriate under what circumstance.

1/23

Fitting models: variability

Describe different measures of dispersion, how they are computed, and how to determine which is most appropriate in any given circumstance. Describe and compute z-scores.

R Lab: Visualizing and summarizing data

Chapter 7, ST21

Understand advantages of “tidy” data
Visualize raw data as histograms and scatterplots

1/28

Probability: basic rules, everyday randomness

Chapter 10, ST21

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.
Learn about common cognitive illusions when thinking about probabilities

1/30

Probability: conditioning and independence

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.

R Lab: Descriptive statistics (central tendency, dispersion)

Chapter 9, ST21

Apply filtering to a dataframe
Compute standard measures of central tendency and dispersion
Apply z-score normalization to data

2/4

Sampling: sampling error and the Central Limit Theorem

Chapter 12, ST21

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

2/6

Sampling: Monte Carlo simulation and bootstrapping

Chapter 14, ST21

Learn about role of Monte Carlo simulation in statistics
Learn about resampling techniques to estimate the sampling distribution

R Lab: Probability

Chapter 11, ST21

Gain additional intuition for probability concepts by conducting simulations in R

2/11

Hypothesis testing: comparing means across two groups

Chapter 16 & 28, ST21

Determine whether a one-sample t-test or two-sample t-test is appropriate for a given hypothesis.
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 p-value as well as common misinterpretations
Distinguish between the two types of error in hypothesis testing, and the factors that determine them.

2/13

Hypothesis testing: comparing means across three or more groups; confidence intervals; effect size

Chapter 28, ST21

Compute a one-sample, two-sample t-test, and ANOVA on relevant datasets, and compute the effect size and confidence intervals associated with each of these tests.

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.

Learning how to read R output

R Lab: Sampling

Chapter 13 & 15, ST21

Gain intuition for Central Limit Theorem by conducting a simulation
Compute standard error of the mean for different sample sizes
Construct 95% confidence intervals

2/18

Resampling; statistical power; limitations of NHST

Describe how resampling can be used to compute a p-value.
Define the concept of statistical power, and compute statistical power for a given statistical test.
Describe the main criticisms of null hypothesis statistical testing

2/20

Quantifying effects: confidence intervals and effect size

Chapter 18, ST21

R Lab: Hypothesis testing

Chapter 17 & 19, ST21

Compute and interpret measures of effect size
Conduct t-test for difference in means and interpret results

2/25

Modeling continuous relationships (continuous outcome; continuous predictor)

Chapter 24, ST21

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.

2/27

Modeling categorical relationship (categorical outcome; categorical predictor)

Chapter 22, ST21

Describe the concept of a contingency table for categorical data.
Describe the concept of the chi-squared test for association and compute it for a given contingency table.

R Lab: ANOVA and correlation

Chapter 25, ST21

Conduct ANOVA for difference in means and interpret results
Compute Pearson correlation and gain intuition for what it means

3/3

General Linear Model: how different types of modeling are related to each other

Chapter 26, ST21

Understand t-tests, ANOVA, regression as variations of the General Linear Model

3/5

Experimental Research: replication/reproducibility; meta-analysis; challenges to cumulative science

Chapter 32, ST21

Describe the concept of P-hacking and its effects on scientific practice
Describe the concept of positive predictive value and its relation to statstical power

R Lab: Linear Regression

Chapter 27, ST21

Fit a linear regression model to data and interpret the results

3/10

Observational Research: strategies for modeling real-world data

Describe how to determine what kind of model to apply to a dataset

3/12

People’s Choice

R Lab: Final Project Workshop

Chapter 30, ST21

Demonstrate the ability to apply statistical models to real data in R