Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.

Hypothesetoetsen en z-scores

Toepassingen van A/B-testen

De steekproefgemiddelde berekenen

Een z-score berekenen

p-waardes

Strafzaken en hypothesetests

Opties

Twee staarten

Linkerstaart

Rechterstaart

Linkerstaart, rechterstaart, twee staarten

p-waarden berekenen

Statistische significantie

Beslissingen op basis van p-waarden

Betrouwbaarheidsintervallen berekenen

Vals-positieve (type I-)fouten

Vals-negatieve (type II-)fouten

Geen fout

Type I- en type II-fouten

Introduction to Hypothesis Testing

Learn how to test for differences in means between two groups using t-tests, and how to extend this to more than two groups using ANOVA and pairwise t-tests.

Performing t-tests

Hypothesis testing workflow

Two sample mean test statistic

Calculating p-values from t-statistics

Why is t needed?

The t-distribution

From t to p

Paired t-tests

Is pairing needed?

Visualizing the difference

Using t.test()

ANOVA tests

Visualizing many categories

Conducting an ANOVA test

Pairwise t-tests

Two-Sample and ANOVA Tests

Learn how to test for differences in proportions between two groups using proportion tests, extended it to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

One-sample proportion tests

t for proportions?

Test for single proportions

Two-sample proportion tests

Test of two proportions

prop_test() for two samples

Chi-square test of independence

The chi-square distribution

How many tails for chi-square tests?

Performing a chi-square test

Chi-square goodness of fit tests

Visualizing goodness of fit

Performing a goodness of fit test

Proportion Tests

Learn about the assumptions made by parametric hypothesis tests and see how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.

Assumptions in hypothesis testing

Common assumptions of hypothesis tests

Testing sample size

The "There is only one test" framework

There is only one test

Specifying and hypothesizing

Continuing the infer pipeline

Generating & calculating

Observed statistic and p-value

Non-parametric ANOVA and unpaired t-tests

Simulation-based t-test

Rank sum tests

Congratulations!

Non-Parametric Tests

Late Shipments

Late shipments Bootstrap Distribution

Democratic Presidential Candidates by County

StackOverflow Survey

Late Shipments w/ Freight Cost Group

Met hypothesetoetsen kun je vragen stellen over je gegevenssets en die op een statistisch verantwoorde manier beantwoorden. In deze cursus leer je hoe en wanneer je veelgebruikte toetsen inzet, zoals t-toetsen, proportietoetsen en chi-kwadraattoetsen. Je krijgt grondig inzicht in hoe ze werken en welke aannames eraan ten grondslag liggen. Je leert ook hoe verschillende hypothesetoetsen samenhangen via het "There is only one test"-framework, en je gebruikt niet-parametrische toetsen waarmee je de vereisten van traditionele hypothesetoetsen kunt omzeilen. Door de hele cursus heen verken je een Stack Overflow-gebruikersenquête en een gegevensset met vertraagde leveringen van medische hulpmiddelen.

Sampling in R

Leer over hypothesetests in R, inclusief het gebruik van t-tests, proportietests en chi-kwadraattests.

Toepassingen van A/B-testen

Hypothesis Testing in R

Praktische interactieve oefening