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.

Hypothesis tests and z-scores

Uses of A/B testing

Calculating the sample mean

Calculating a z-score

p-values

Criminal trials and hypothesis tests

Left tail, right tail, two tails

Calculating p-values

Statistical significance

Decisions from p-values

Calculating confidence intervals

Type I and type II errors

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.

Asumsi dalam pengujian hipotesis

Asumsi umum dalam uji hipotesis

Menguji ukuran sampel

Kerangka "Hanya Ada Satu Uji"

There is only one test

Menentukan spesifikasi dan hipotesis

Melanjutkan pipeline infer

Membuat & menghitung

Statistik teramati dan p-value

ANOVA non-parametrik dan uji t tidak berpasangan

Uji t berbasis simulasi

Uji jumlah peringkat

Selamat!

Non-Parametric Tests

Late Shipments

Late shipments Bootstrap Distribution

Democratic Presidential Candidates by County

StackOverflow Survey

Late Shipments w/ Freight Cost Group

Pengujian hipotesis memungkinkan Anda mengajukan pertanyaan tentang himpunan data Anda dan menjawabnya secara ketat secara statistik. Dalam kursus ini Anda akan mempelajari cara dan waktu menggunakan uji umum seperti uji t, uji proporsi, dan uji chi-square. Anda akan mendapatkan pemahaman mendalam tentang cara kerjanya dan asumsi yang mendasarinya. Anda juga akan mempelajari bagaimana berbagai uji hipotesis saling berkaitan menggunakan kerangka "Hanya ada satu uji", serta menggunakan uji nonparametrik yang memungkinkan Anda menghindari persyaratan uji hipotesis tradisional. Sepanjang kursus, Anda akan mengeksplorasi survei pengguna Stack Overflow dan himpunan data keterlambatan pengiriman perlengkapan medis.

Sampling in R

Pelajari uji hipotesis di R, termasuk kapan dan cara menggunakan uji t, uji proporsi, dan uji chi-kuadrat.

Asumsi umum dalam uji hipotesis

Pengujian Hipotesis di R

Latihan interaktif praktis