Types of Sampling | Introduction to Data Mining part 13 | Summary and Q&A

TL;DR

This content explains the different types of sampling methods, including simple random sampling, stratified sampling, and sampling with or without replacement, and the importance of selecting an appropriate sample size.

Key Insights

• 🔥 Different types of sampling include simple random sampling and stratified sampling, with stratified sampling allowing for different sized partitions and sample sizes.
• 💡 Sampling can be done with or without replacement, with sampling without replacement being the more common method.
• 📊 Sampling with replacement and without replacement have different mathematical results and are used in different contexts.
• 🌱 Sample size is crucial, as too small of a sample can lead to loss of important information, while too large of a sample may be inefficient.
• 💪 It is important to strike a balance between having a large enough sample for analysis and exploration efficiency, while still capturing relevant information.
• ⚙️ Experimentation with different sample sizes is necessary to determine when information starts to disappear and the optimal sample size to use.
• 🧪 Subsampling by a quarter can still capture important structures, but can result in the loss of background information such as sine waves.
• 📏 There is no fixed rule for determining the ideal sample size, and it requires experimentation and exploration to find the right balance.

Transcript

There are several different types of sampling that are important. That will come up as we talk about over the course of the boot camp. So there's simple random sampling, where there's an equal probability of selecting any particular item. There's stratified sampling, where we split the data into several partitions and draw out random samples from e... Read More

Questions & Answers

Q: What is the difference between simple random sampling and stratified sampling?

Simple random sampling involves randomly selecting items with an equal probability, while stratified sampling involves splitting the data into partitions and selecting random samples from each partition. Stratified sampling allows for different sized partitions and is not equivalent to simple random sampling in most cases.

Q: What is the difference between sampling with replacement and without replacement?

Sampling with replacement involves selecting items and putting them back into the population, allowing for repeated selection of the same items. Sampling without replacement does not put the selected items back into the population, which changes the probabilities for subsequent selections.

Q: Why is selecting an appropriate sample size important in data analysis?

Selecting an appropriate sample size is crucial because a sample that is too small can lead to the loss of important information and introduce biases in the analysis. A sample should be small enough for efficient processing but large enough to accurately represent the population.

Q: What is the difference between equal-sized partitions and different-sized partitions in stratified sampling?

Equal-sized partitions in stratified sampling mean that each partition contains the same number of items. Different-sized partitions, on the other hand, have partitions of varying sizes, allowing for more flexibility in sampling from different subsets of the data.

Q: Is there a specific rule of thumb for determining the appropriate sample size?

Unfortunately, there is no one-size-fits-all rule for determining the appropriate sample size. It is best to experiment with different sample sizes and assess when the information starts to disappear or become unreliable.

Summary & Key Takeaways

• There are different types of sampling methods used in data analysis, including simple random sampling and stratified sampling.

• Simple random sampling involves selecting items randomly with an equal probability, while stratified sampling involves dividing the data into partitions and selecting random samples from each partition.

• Sampling can also be done with or without replacement, with replacement allowing for repeated selection of items, while without replacement does not replace the selected items.