Menerapkan koreksi Bonferroni

Setelah mengidentifikasi perbedaan yang signifikan antar kelompok terapi dengan Tukey's HSD, kita ingin menegaskan temuan tersebut menggunakan koreksi Bonferroni. Koreksi Bonferroni adalah penyesuaian statistik yang konservatif untuk mengatasi masalah perbandingan berganda. Metode ini menurunkan peluang terjadinya hasil positif palsu dengan menyesuaikan tingkat signifikansi. Dalam konteks studi Anda tentang efektivitas CBT, DBT, dan ACT, menerapkan koreksi Bonferroni akan membantu memastikan bahwa perbedaan signifikan yang Anda amati antar kelompok terapi tidak terjadi secara kebetulan.

DataFrame therapy_outcomes telah dimuat kembali bersama dengan pandas as pd, from scipy.stats import ttest_ind, dan from statsmodels.sandbox.stats.multicomp import multipletests.

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

Petunjuk latihan

Lakukan uji t independen untuk semua pasangan kelompok terapi dalam therapy_pairs dan tambahkan p-value (p_val) ke daftar p_values.
Terapkan koreksi Bonferroni untuk menyesuaikan p-value dari beberapa pengujian dan cetak hasilnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

p_values = []

therapy_pairs = [('CBT', 'DBT'), ('CBT', 'ACT'), ('DBT', 'ACT')]

# Conduct t-tests and collect P-values
for pair in ____:
    group1 = therapy_outcomes[therapy_outcomes['Therapy_Type'] == ____]['Anxiety_Reduction']
    group2 = therapy_outcomes[therapy_outcomes['Therapy_Type'] == ____]['Anxiety_Reduction']
    t_stat, p_val = ____(group1, group2)
    p_values.____(p_val)

# Apply Bonferroni correction
print(____(____, alpha=0.05, method='____')[1])

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Perancangan Eksperimen dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Factorial designs: principles and applications Exercise 2: Understanding marketing campaign effectiveness Exercise 3: Heatmap of campaign interactions Exercise 4: Factorial designs and randomized block designs Exercise 5: Randomized block design: controlling variance Exercise 6: Implementing a randomized block design Exercise 7: Visualizing productivity within blocks by incentive Exercise 8: ANOVA within blocks of employees Exercise 9: Covariate adjustment in experimental design Exercise 10: Importance of covariates Exercise 11: Covariate adjustment with chick growth

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Memilih uji statistik yang tepat Exercise 2: Memilih uji yang tepat: petrokimia Exercise 3: Memilih uji yang tepat: sumber daya manusia Exercise 4: Memilih uji yang tepat: keuangan Exercise 5: Analisis post-hoc setelah ANOVA Exercise 6: ANOVA untuk pengobatan kecemasan Exercise 7: Menerapkan Tukey's HSD Exercise 8: Menerapkan koreksi Bonferroni

Latihan Saat Ini

Exercise 9: P-value, alpha, dan galat Exercise 10: Menganalisis daya tahan mainan Exercise 11: Memvisualisasikan perbedaan ketahanan Exercise 12: Peran tingkat signifikansi Exercise 13: Analisis daya: ukuran sampel dan efek Exercise 14: Tujuan ukuran efek Exercise 15: Mengestimasi ukuran sampel yang diperlukan untuk studi energi

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Synthesizing insights from complex experiments Exercise 2: Visualizing loan approval yield Exercise 3: Exploring customer satisfaction Exercise 4: Effectively communicating experimental data Exercise 5: Addressing complexities in experimental data Exercise 6: Check for heteroscedasticity in shelf life Exercise 7: Exploring and transforming shelf life data Exercise 8: Applying nonparametric tests in experimental analysis Exercise 9: Visualizing and testing preservation methods Exercise 10: Further analyzing food preservation techniques Exercise 11: Congratulations!