what is stratified sampling
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Stratified Sampling | A Comprehensive Guide with Examples

What Stratified Sampling Is? 

Imagine you’re surveying a school fair to find out everyone’s favourite movie genre. But instead of just asking whoever walks by, you want to make sure you hear from different groups (like kids, teenagers, adults).

Stratified sampling is like dividing the fairgoers into those groups (called strata). Then, you randomly pick a few people from each group to ask their opinion. This way, you get a “fair mix” of everyone’s choices, not just the most vocal group.

Think of it like picking toppings for a pizza. You wouldn’t just grab everything at once, you’d choose some veggies, some meats, and some cheeses to get a balanced mix. Stratified sampling does the same for surveys, ensuring a good mix of different “toppings” (like age groups) in your results.

It’s a clever way to get accurate information about different groups within a bigger population. For example, getting the perfect pizza for everyone at the fair.

When’s The Right Time To Use Stratified Sampling

Imagine you’re a detective investigating a mysterious case at a bustling carnival. You need to understand the preferences of all the diverse groups present: thrill-seeking teenagers, families with young children, costume-clad partygoers, and even food vendors. Simply grabbing the first few people you see might miss crucial clues hidden within specific groups.

Stratified sampling is your secret weapon in this scenario. It’s like dividing the carnival into sections based on shared characteristics, like the “Thrill Zone” for adventurers or the “Foodie Fairway” for snack enthusiasts. These sections are called strata. 

Then, you cleverly pick a handful of individuals from each section, ensuring everyone gets a chance to share their experience, not just the most vocal bunch by the roller coaster.

Here’s Where It Becomes More Useful:

Diverse crowds: Imagine the carnival teeming with unique groups, just like real-world populations. Stratified sampling ensures each group’s voice is heard, painting a more complete picture.

Uncovering hidden tastes: It’s especially helpful for understanding the preferences of smaller groups, like the cotton candy-loving toddlers who might get overlooked otherwise.

Comparing preferences: Do you suspect different groups might have different experiences like teenagers craving excitement and families prioritising safety? Stratified sampling lets you compare their perspectives accurately.

But remember, even the best detective needs the right tools:

Planning the investigation: Before questioning anyone, you need to categorise everyone at the carnival by thinking of colourful bandanas each for a different section. This can be tricky, but crucial for accurate results.

Guest list: Just like having a list of everyone at the carnival (think a map with everyone’s location), you need information about each group to draw your samples fairly. Missing information can lead to skewed results.

Clever calculations: The analysis might involve some fancy maths, but fear not. Just like the carnival has its tech whiz running the lights, computers can handle the complex calculations for you.

Stratified Sampling Advantages And Disadvantages

There is no doubt that there are various surveys that use stratified sampling for effective benefits. So, some of the beneficial factors of using stratified sampling are given below: 

Advantages Of Stratified Sampling:

  • Less mistake-making: It reduces the chance of missing specific groups, like only asking adults and forgetting the kids’ preferences.
  • More voices heard: It ensures everyone gets a chance to share, giving you a better picture of what everyone likes.
  • Accurate results: If each group is similar within itself but different from other groups (like kids loving cartoons and adults preferring dramas), this method gives you very precise results.

Disadvantages Of Stratified Sampling:

  • Finding Layers: You need to know your crowd! Dividing people into clear groups (like age or location) can be tricky, especially if they overlap (think kids who are also athletes). If this happens, some people get picked more often, making the results inaccurate.
  • Double-Duty People: Imagine someone who fits into two groups, like a young athlete. Deciding which group to put them in can be confusing, again skewing the results.
  • Time and Money: Adding this extra step to your study takes more time and resources. Think of it like setting up different booths at a party for each group. It takes effort, but can be worth it for the right crowd.
  •  Math Magic: Analysing this method uses special calculations that can be a bit complex. But don’t worry, computers can handle them.

Example Of Stratified Sampling

Imagine you’re asking people at a party what their favourite movie genre is. Asking whoever happens to be nearby might miss important groups, like the sci-fi fans or the rom-com enthusiasts.

Stratified sampling is like splitting the partygoers into groups based on what they like (sci-fi fans, comedy lovers, etc.). Then, you ask a few people from each group what their favourite genre is. This way, everyone gets a chance to share their favourite, not just the loudest group.

Think of it like making sure every table at a fancy restaurant gets a different course to try. Each table (like a group) gets a representative sample of the menu (like different movie genres), so you get a good taste of everyone’s preferences. It’s a clever way to hear from everyone, just like getting the best mix of movie recommendations at a party.

Stratified Sampling: Proportionate vs. Disproportionate Approaches

In research methodology, stratified sampling emerges as a powerful tool for gathering representative data from diverse populations. By dividing the population into subpopulations (strata) based on shared characteristics, researchers can achieve greater precision and insight compared to simple random sampling. 

However, within the realm of stratified sampling lies a critical decision: which sampling approach to employ? Two main methods dominate the scene: proportionate and disproportionate sampling.

Proportionate Sampling: A Proportional Slice of the Pie

This approach mirrors baking a balanced pizza. Imagine subdividing the pizza based on the population’s proportional breakdown – say, 20% children, 40% adults, and 40% vegetarians. Proportionate sampling then draws a sample from each stratum that reflects its size in the overall population. 

In our pizza analogy, this translates to 20% of the cheese slices going to children, 40% to adults, and 40% to vegetarians. This technique ensures every group has a voice, painting a representative picture of the entire population. It’s ideal for understanding general trends and preferences across diverse groups.

Disproportionate Sampling: Zooming in on Specific Toppings

Now, imagine you’re particularly fond of olives. Disproportionate sampling allows you to indulge in this preference! This method deviates from proportional representation, deliberately oversampling specific strata to gain deeper insights into their characteristics. 

Continuing the pizza analogy, you might choose to add extra olives to your slice, even if they represent a smaller group within the population. This prioritises obtaining detailed data from specific cohorts, even if they aren’t the majority. By doing so, you gain a richer understanding of their unique preferences and experiences.

Choosing the Right Approach: A Matter of Taste

The optimal sampling method hinges on your research question. If your goal is to capture a broad, representative picture of the population’s preferences, proportionate sampling reigns supreme. However, if you’re drawn to delve deeper into specific groups, like understanding the dietary needs of vegetarians, disproportionate sampling allows you to tailor your inquiry for targeted insights.

The Method Of Using Stratified Sampling

Imagine you’re making a delicious fruit salad for your friends, but you want everyone to have a chance to taste their favourites. You wouldn’t just grab random fruits, right? That might leave some people sad with just apples when they love mangoes.

Stratified sampling is like making sure everyone gets to try their favourites. Here’s how it works:

  1. Separate the fruits: First, you divide your friends into groups based on their preferences. Maybe one group loves berries, another prefers citrus, and others enjoy tropical fruits. These groups are called strata.
  2. Pick from each basket: Next, you grab a smaller bunch of fruits from each group. Think of it like taking a handful of berries, a few oranges, and some pineapple chunks. This ensures everyone’s favourite type of fruit gets represented in the salad.
  3. Mix and enjoy: Finally, you put all the fruits together in a delicious mix. This gives everyone a chance to taste different favourites and enjoy the variety.

This is the basic idea behind stratified sampling. It’s like making sure everyone’s voice is heard, even if some groups are smaller than others. And while the actual formula might involve some maths, remember, the main goal is to ensure everyone gets a fair taste of the “information salad” you’re creating.

The Origins Of Stratified Sampling 

Stratified sampling is your secret weapon in this case! It’s like dividing the fair-goers into groups based on what they might like: the “Chocolate Champions,” the “Fruity Fanatics,” and the “Vanilla Vanguard.” Then, you question some people from each group to get a complete picture of everyone’s preferences.

This clever method has a long history, though not as exciting as a detective story! The earliest ideas can be traced back hundreds of years ago, with mathematicians like Jakob Bernoulli figuring out how to sample groups evenly. 

By the 1940s and 50s, statisticians like Morris Hansen and William Deming refined this idea for use in real-world studies, like understanding crop yields or preferences for new products.

Think of it this way: before stratified sampling, researchers were like throwing darts blindfolded at a dartboard, hoping to hit the target. This new method allowed them to see the board, aim at specific sections, and get much closer to understanding the whole picture.

Today, stratified sampling is used in all sorts of studies, from understanding voting preferences to measuring student performance across different schools. It’s a powerful tool for ensuring everyone’s voice is heard, just like making sure every ice cream flavour gets a chance to shine at the fair. 

Remember, even though the origins might be a bit dusty, the idea of understanding different groups within a whole is as sweet and important as ever.

The Evaluation Of Stratified Sampling

First, you create “strata” (fancy word for groups) based on preferences: cheese fans, veggie lovers, meat maestros, and maybe even an anchovy adventurer. Then, you pick a few people from each group to try different pizzas, ensuring everyone gets a yummy voice.

But how do we know this works? Here’s the evaluation stage:

  • Did everyone get a slice? Did each group have a fair chance to share their preferences, like ensuring the anchovy fan gets to shout about their love (even if just one person)?
  • Is the pizza (data) representative? Does the mix of opinions reflect the whole party (population), or did we miss a hidden group of pineapple enthusiasts?
  • Are the slices big enough? Do we have enough data from each group to understand their unique preferences, or does one group have only a tiny, lonely slice?

Evaluating stratified sampling involves checking if it truly captured the diverse voices within the population, just like making sure everyone at the party can share their love for their favourite pizza toppings. It’s not just about having fun (though pizza helps), it’s about getting accurate and complete information for your research.


So, we’ve demystified the world of stratified sampling, from its delicious pizza analogy to its detective-worthy origins! Remember, it’s all about ensuring everyone gets a voice, whether it’s the cheese fans at your research “party” or the adventurous anchovy lovers.

This clever method helps you paint a more accurate picture of the whole population, avoiding the pitfall of just listening to the loudest voices. But like any good recipe, it requires careful planning and consideration. White Rose Math might involve some “mathy” steps, but don’t let that daunt you Powerful tools like computers are there to help crunch the numbers.

Ultimately, choosing the right sampling method depends on your research question. If you’re looking for a comprehensive picture with everyone included, stratified sampling is your best friend. Just remember, it’s not a one-size-fits-all solution. So, the next time you’re faced with diverse data, think of the party analogy and choose the method that ensures everyone gets to share their slice of the research pie.