Stratified sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
Stratified sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
The practical value of Stratified sampling comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.
A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.
Define the target population, sampling frame and selection unit; choose the selection rule; record inclusion probabilities or strata where relevant; then inspect coverage and non-response.
The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.
Suppose a retailer wants customer-satisfaction evidence across regions. Sampling customers proportionally may under-represent small regions, whereas stratification can guarantee enough observations per region.
A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.
Keep the example small enough that you can inspect each stage manually.
# Purpose: demonstrate Stratified sampling with a small, inspectable example.
# Follow the comments and printed stages to connect each operation with its result.
# Import the library or helper used in this example.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import numpy as np
# Import the library or helper used in this example.
# Step 2 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.model_selection import StratifiedKFold
# Create the numerical values used in the calculation.
# Step 3 — Compute the right-hand expression and store its result in `X` for the next step.
X = np.arange(48).reshape(24,2)
# Step 4 — Construct `y` as an array so vectorised numerical operations can be applied consistently.
y = np.array([0]*12 + [1]*12)
# Store this intermediate value with a descriptive name for the next step.
# Step 5 — Compute the right-hand expression and store its result in `cv` for the next step.
cv = StratifiedKFold(n_splits=4, shuffle=True, random_state=7)
# Print this intermediate result so you can verify the workflow step by step.
# Step 6 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Samples:", len(y), "class counts:", np.bincount(y).tolist())
# Iterate through the current values one item or step at a time.
# Step 7 — Iterate through the collection so the indented block is applied once for each item.
for fold,(tr,va) in enumerate(cv.split(X,y),1):
# Print this intermediate result so you can verify the workflow step by step.
# Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
print(f"STEP 2 · Fold {fold}: train={len(tr)} valid={len(va)} valid_classes={np.bincount(y[va]).tolist()}")
# Print this intermediate result so you can verify the workflow step by step.
# Step 9 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Each sample is validation data once across the four folds.")STEP 1 · Samples: 24 class counts: [12, 12] STEP 2 · Fold 1: train=18 valid=6 valid_classes=[3, 3] STEP 2 · Fold 2: train=18 valid=6 valid_classes=[3, 3] STEP 2 · Fold 3: train=18 valid=6 valid_classes=[3, 3] STEP 2 · Fold 4: train=18 valid=6 valid_classes=[3, 3] STEP 3 · Each sample is validation data once across the four folds.