How does hypothesis testing work and what problems can it solve? To find out, you’ll walk through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-values, and false negative and false positive errors.

Uji hipotesis dan z-score

Kegunaan pengujian A/B

Menghitung mean sampel

Menghitung z-score

p-value

Persidangan pidana dan uji hipotesis

Opsi

Dua ekor

Ekor kiri

Ekor kanan

Ekor kiri, ekor kanan, dua ekor

Menghitung p-value

Signifikansi statistik

Keputusan dari nilai p

Menghitung interval kepercayaan

Kesalahan positif palsu (Tipe I)

Kesalahan negatif palsu (Tipe II)

Bukan kesalahan

Kesalahan tipe I dan tipe II

Hypothesis Testing Fundamentals

In this chapter, you’ll learn how to test for differences in means between two groups using t-tests and 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 ttest()

ANOVA tests

Visualizing many categories

Conducting an ANOVA test

Pairwise t-tests

Two-Sample and ANOVA Tests

Now it’s time to test for differences in proportions between two groups using proportion tests. Through hands-on exercises, you’ll extend your proportion tests 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

proportions_ztest() 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

Finally, it’s time to learn about the assumptions made by parametric hypothesis tests, and see how non-parametric tests can be used when those assumptions aren't met.

Assumptions in hypothesis testing

Common assumptions of hypothesis tests

Testing sample size

Non-parametric tests

Which parametric test?

Wilcoxon signed-rank test

Non-parametric ANOVA and unpaired t-tests

Wilcoxon-Mann-Whitney

Kruskal-Wallis

Congratulations!

Non-Parametric Tests

Late Shipments

Stack Overflow

U.S. Democrat Votes 2012/2016

U.S. Republican Votes 2008/2012

Course Glossary

Pengujian hipotesis memungkinkan Anda menjawab pertanyaan tentang himpunan data secara ketat secara statistik. Dalam kursus ini, Anda akan mengembangkan keterampilan analitis Python saat mempelajari cara dan waktu menggunakan uji umum seperti uji t, uji proporsi, dan uji chi-square. Dengan bekerja menggunakan data dunia nyata, termasuk umpan balik pengguna Stack Overflow dan data rantai pasok untuk pengiriman perlengkapan medis, Anda akan memperoleh pemahaman mendalam tentang cara kerja uji tersebut dan asumsi kunci yang mendasarinya. Anda juga akan mengetahui bagaimana uji non-parametrik dapat digunakan untuk melampaui keterbatasan uji hipotesis tradisional.

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Glosarium kursus dapat ditemukan di kanan pada bagian sumber daya.
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Sampling in Python

Pelajari cara dan waktu menggunakan uji hipotesis umum seperti t-test, uji proporsi, dan chi-square.

Pengujian Hipotesis dengan Python

Associate Data Scientist  dalam Python

Analis Data  dalam Python

Dasar-Dasar Statistik  dalam Python

Persidangan pidana dan uji hipotesis

Pengujian Hipotesis dengan Python

Latihan interaktif praktis