DA0-002 Data Acquisition and Preparation Practice Question
An e-commerce company wants to analyze sales performance across product categories. The dataset includes transaction amounts and a column 'category' with values (Electronics, Clothing, Home). The analyst decides to use stratified sampling to ensure proportional representation. Which THREE steps are required to implement this? (Select THREE).
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Calculate the proportion of each category in the population
Stratified sampling requires first partitioning the population into homogeneous subgroups, so option C is correct: dividing the dataset into three strata based on the 'category' column (Electronics, Clothing, Home) creates the strata. Next, option A is correct because the analyst must calculate each category's proportion of the total population to determine how many samples each stratum should contribute for proportional representation. Option B is also correct because, after determining stratum sizes, a random sample must be drawn from each stratum with size proportional to its population proportion, which is the defining allocation step of proportional stratified sampling. Option D is incorrect because selecting every 10th transaction describes systematic sampling, not stratified sampling, and it ignores the category strata. Option E is incorrect because merging all categories into one group and performing simple random sampling removes the stratification and does not guarantee proportional representation across categories.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Calculate the proportion of each category in the population
Why this is correct
Stratified sampling requires knowing each stratum's share of the population to allocate sample sizes proportionally. Calculating the proportion of each category establishes those weights, which the subsequent per-stratum sampling step uses to preserve the dataset's category distribution.
- ✓
Take a random sample from each stratum with size proportional to its population proportion
Why this is correct
After proportions are known, drawing a random sample within each stratum sized to match its population proportion guarantees the sample mirrors the category distribution. This is the defining mechanism of stratified sampling, satisfying the proportional representation constraint.
- ✓
Divide the dataset into three strata based on category
Why this is correct
Stratified sampling requires partitioning the population into homogeneous subgroups before drawing samples. Splitting on the 'category' column creates the three strata (Electronics, Clothing, Home), satisfying the proportional-representation constraint by ensuring each category is sampled in line with its share of transactions.
- ✗
Select every 10th transaction from the entire dataset
Why it's wrong here
Selecting every 10th transaction is systematic sampling, which draws from the whole dataset without partitioning by the category column. Stratified sampling requires splitting into strata first, then sampling within each. Systematic sampling suits ordered lists where even coverage across a sequence is wanted, not proportional representation across groups.
- ✗
Combine all categories into a single group and perform simple random sampling
Why it's wrong here
Pooling categories into one group and sampling randomly destroys the strata, so proportional representation by category cannot be guaranteed. Stratified sampling keeps each category as a separate stratum and samples within it. Simple random sampling over a homogeneous population is the right choice when subgroup proportions do not matter.
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Written by Johnson Ajibi, MSc IT Security
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