Sebenarnya, apa itu power?

Beberapa istilah terkait power bisa cukup membingungkan. Misalnya, power dari uji t dua sampel adalah:

Peluang menolak hipotesis nol demi hipotesis alternatif, dengan asumsi memang ada perbedaan mean populasi antara kedua kelompok.

Cukup berbelit, bukan!

Bayangkan Anda menganalisis sebuah kampanye pemasaran dan ingin menyimpulkan apakah kampanye tersebut meningkatkan pengeluaran pelanggan. Anda melakukan uji t dua sampel (pelanggan yang terekspos kampanye dibandingkan pelanggan yang tidak terekspos) untuk membandingkan rata-rata jumlah dolar yang dibelanjakan oleh tiap kelompok.

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Dasar-dasar Inferensi di Python

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Latihan ini adalah bagian dari kursus

Dasar-dasar Inferensi di Python

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In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.

Exercise 1: Statistical inference and random sampling Exercise 2: Sampling and point estimates Exercise 3: Repeated sampling, point estimates and inference Exercise 4: Sampling and bias Exercise 5: Visualizing samples Exercise 6: Inference and bias Exercise 7: Confidence intervals and sampling Exercise 8: Normal sampling distributions Exercise 9: Calculating confidence intervals Exercise 10: Drawing conclusions from samples

Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.

Exercise 1: Normality tests Exercise 2: Testing for normality Exercise 3: Distribution of errors Exercise 4: Fitting a normal distribution Exercise 5: Correlation tests Exercise 6: Testing for correlation Exercise 7: Autocorrelation Exercise 8: Explained variance Exercise 9: Parametric tests Exercise 10: Equal variance Exercise 11: Normality of groups Exercise 12: ANOVA Exercise 13: Non-parametric tests Exercise 14: Comparing rankings Exercise 15: Comparing medians

In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.

Exercise 1: Ukuran efek Exercise 2: Ukuran efek untuk mean Exercise 3: Ukuran efek untuk korelasi Exercise 4: Ukuran efek untuk variabel kategorikal Exercise 5: Perbandingan berganda dan koreksi Exercise 6: Masalah perbandingan berganda Exercise 7: Koreksi Bonferroni–Holm Exercise 8: Kekuatan suatu uji Exercise 9: Sebenarnya, apa itu power?

Latihan Saat Ini

Exercise 10: Power untuk perancangan eksperimen Exercise 11: Menghitung power dan ukuran sampel

You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.

Exercise 1: Bootstrapping Exercise 2: Bootstrap confidence intervals Exercise 3: Bootstrapping vs. normality Exercise 4: Combining evidence from p-values Exercise 5: Fisher's method in SciPy Exercise 6: Inference using Fisher's method Exercise 7: Summarizing Fisher's method Exercise 8: Permutation tests Exercise 9: Permutation tests for correlations Exercise 10: Permutation tests and bootstrapping Exercise 11: Analyzing skewed data with a permutation test Exercise 12: Course wrap-up video