Explore Library
AI FundamentalsProbability, Statistics, and Linear Algebra for AI

41 items

1

Confidence Intervals & the Sampling Distribution

Flashcard
2

Hypothesis Testing in A/B Experiments

Flashcard
3

Distributions, Sampling & Confidence Intervals

Slides / Video
4

Hypothesis Testing & A/B Design

Slides / Video
5

Interpreting the p-value Wrong

Code Quiz
6

Interpreting P-values Correctly

Quiz
7

Buggy Standard Deviation Function

Code Quiz
8

Mean vs Median in Skewed Data

Quiz
9

P-value Significance Check Bug

Code Quiz
10

Understanding P-values and Correlation

Quiz
11

Population Variance Bug

Code Quiz
12

Mean vs Median in Skewed Data

Quiz
13

Hypothesis Testing, P-values & Causation

Slides / Video
14

P-values and Correlation vs Causation

Flashcard
15

Hypothesis Testing, p-values & Intervals

Slides / Video
16

P-values & Confidence Intervals

Flashcard
17

Extracting an Eigenvector from np.linalg.eig

Code Quiz
18

Data as Vectors and Matrices

Slides / Video
19

Gradients and Partial Derivatives

Slides / Video
20

Singular Value Decomposition Basics

Slides / Video
21

Matrix Transpose and Inverse

Slides / Video
22

Linear Independence, Span, and Basis

Slides / Video
23

Vector Norms and Distance Metrics

Slides / Video
24

Dot Product and Vector Similarity

Slides / Video
25

Matrix Multiplication in AI

Slides / Video
26

Matrices and Matrix Operations

Slides / Video
27

Vectors and Vector Operations

Slides / Video
28

Confidence Intervals

Slides / Video
29

Hypothesis Testing and p-values

Slides / Video
30

Sampling and the Central Limit Theorem

Slides / Video
31

Descriptive Statistics: Center and Spread

Slides / Video
32

Maximum Likelihood Estimation

Slides / Video
33

Joint, Marginal, Conditional Distributions

Slides / Video
34

Covariance and Correlation

Slides / Video
35

Expectation, Variance, Standard Deviation

Slides / Video
36

Bayes' Theorem in AI

Slides / Video
37

Conditional Probability and Independence

Slides / Video
38

Gaussian, Bernoulli, Binomial Distributions

Slides / Video
39

Discrete vs Continuous Distributions

Slides / Video
40

Random Variables and Distributions

Slides / Video
41

Eigenvalues and Eigenvectors Basics

Slides / Video
Code Quiz

Interpreting the p-value Wrong

A hypothesis test flips the significance comparison, drawing the opposite conclusion about the null hypothesis.

Codepython
from scipy import stats

# A/B test: did the new model improve click-through rate?
# Two-sample t-test comparing conversion samples
control = [0.11, 0.09, 0.10, 0.12, 0.08]
variant = [0.14, 0.13, 0.15, 0.12, 0.16]

alpha = 0.05
t_stat, p_value = stats.ttest_ind(control, variant)

if p_value > alpha:
    print('Reject H0: the variant significantly changed CTR')
else:
    print('Fail to reject H0: no significant difference')

What is the bug in this hypothesis-testing code?