Skip to main content
HomePythonHypothesis Testing in Python

Hypothesis Testing in Python

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Comece O Curso Gratuitamente
4 Horas15 Videos50 Exercicios
30.116 AprendizesTrophyDeclaração de Realização

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTreinar 2 ou mais pessoas?Experimente o DataCamp For Business

Loved by learners at thousands of companies

Descrição do Curso

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. In this course, you'll grow your Python analytical skills as you learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. Working with real-world data, including Stack Overflow user feedback and supply-chain data for medical supply shipments, you'll gain a deep understanding of how these tests work and the key assumptions that underpin them. You'll also discover how non-parametric tests can be used to go beyond the limitations of traditional hypothesis tests.
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados
Experimente O DataCamp for BusinessPara uma solução sob medida , agende uma demonstração.

Nas seguintes faixas

Certificação disponível

Analista de dados com Python

Ir para a trilha
Certificação disponível

Cientista de dados associado em Python

Ir para a trilha

Fundamentos de estatística com Python

Ir para a trilha
  1. 1

    Hypothesis Testing Fundamentals

    Grátis

    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.

    Reproduzir Capítulo Agora
    Hypothesis tests and z-scores
    50 xp
    Uses of A/B testing
    50 xp
    Calculating the sample mean
    100 xp
    Calculating a z-score
    100 xp
    p-values
    50 xp
    Criminal trials and hypothesis tests
    50 xp
    Left tail, right tail, two tails
    100 xp
    Calculating p-values
    100 xp
    Statistical significance
    50 xp
    Decisions from p-values
    50 xp
    Calculating a confidence interval
    100 xp
    Type I and type II errors
    100 xp
  2. 2

    Two-Sample and ANOVA Tests

    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.

    Reproduzir Capítulo Agora
    Performing t-tests
    50 xp
    Hypothesis testing workflow
    100 xp
    Two sample mean test statistic
    100 xp
    Calculating p-values from t-statistics
    50 xp
    Why is t needed?
    50 xp
    The t-distribution
    50 xp
    From t to p
    100 xp
    Paired t-tests
    50 xp
    Is pairing needed?
    100 xp
    Visualizing the difference
    100 xp
    Using ttest()
    100 xp
    ANOVA tests
    50 xp
    Visualizing many categories
    100 xp
    Conducting an ANOVA test
    100 xp
    Pairwise t-tests
    100 xp
  3. 3

    Proportion 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.

    Reproduzir Capítulo Agora
    One-sample proportion tests
    50 xp
    t for proportions?
    50 xp
    Test for single proportions
    100 xp
    Two-sample proportion tests
    50 xp
    Test of two proportions
    100 xp
    proportions_ztest() for two samples
    100 xp
    Chi-square test of independence
    50 xp
    The chi-square distribution
    50 xp
    How many tails for chi-square tests?
    50 xp
    Performing a chi-square test
    100 xp
    Chi-square goodness of fit tests
    50 xp
    Visualizing goodness of fit
    100 xp
    Performing a goodness of fit test
    100 xp
  4. 4

    Non-Parametric 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.

    Reproduzir Capítulo Agora
    Assumptions in hypothesis testing
    50 xp
    Common assumptions of hypothesis tests
    50 xp
    Testing sample size
    100 xp
    Non-parametric tests
    50 xp
    Which parametric test?
    50 xp
    Wilcoxon signed-rank test
    100 xp
    Non-parametric ANOVA and unpaired t-tests
    50 xp
    Wilcoxon-Mann-Whitney
    100 xp
    Kruskal-Wallis
    100 xp
    Congratulations!
    50 xp
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados

Nas seguintes faixas

Certificação disponível

Analista de dados com Python

Ir para a trilha
Certificação disponível

Cientista de dados associado em Python

Ir para a trilha

Fundamentos de estatística com Python

Ir para a trilha

Conjuntos De Dados

Late ShipmentsStack OverflowU.S. Democrat Votes 2012/2016U.S. Republican Votes 2008/2012

Colaboradores

Collaborator's avatar
Dr. Chester Ismay
Collaborator's avatar
Amy Peterson
Collaborator's avatar
Izzy Weber

Pre Requisitos

Sampling in Python
James Chapman HeadshotJames Chapman

Curriculum Manager, DataCamp

Veja Mais

O que os outros alunos têm a dizer?

    Cadastre-se mais 13 milhões de alunos e comece Hypothesis Testing in Python Hoje!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.