# Types of Sampling and Sampling Distribution

## What is Sampling?

Sampling is the process of

**selecting a subset of data**from a larger population to**analyze and draw conclusions**about the entire population.### Random Sampling

- In random sampling, everyone in the universe has an equal chance of being selected..
**For instance,**if you want to study the heights of students in a school, you could randomly select a certain number of students from each grade.

### Stratified Sampling

- In stratified sampling, the population is
**divided into several groups or strata**and a sample is selected from each stratum. **For example**, if you want to survey people's opinions on a product,- you might divide the population into
**age groups**and then randomly select individuals from each group.

### Convenience Sampling

- Simple criteria include selecting people who are easy to find or contact.
**For instance,**conducting a survey by asking people passing by on the street.**Example: I**n data visualization, suppose you are analyzing customer satisfaction ratings for a product.- You might randomly sample a certain number of customers from your entire customer base and visualize their feedback using charts or graphs.

### Systematic Sampling

- Good sampling consists of selecting every nth element from a list or population, starting from a matching point.
- It provides a structured way of sampling.
- Example: In a manufacturing plant, if you want to check the quality of products,
- you might use systematic sampling by selecting every 10th product from the production line for inspection.

### Cluster Sampling

- Cluster sampling involves dividing the population into
**clusters or groups**based on**geographical or other characteristics**, - The entire group is then randomly selected for the sample.
- In a survey about
**city demographics,**you might use cluster sampling by dividing the city into - neighborhoods (clusters) and then randomly selecting a few neighborhoods to survey residents.

## Sampling Distribution

A sampling distribution is a d

**istribution of sample statistics obtained from multiple samples**taken from the same population.### Central Limit Theorem

- The central limit theorem says it doesn't matter what the population distribution
**is.** - The sampling distribution of the sample mean tends to approximate a normal distribution when the sample size is large.
**For example,**even if you have a very skewed population of ages, averaging the ages from large random samples will result in a bell curve.

### Standard Error

- The standard error measures the variability of sample means in a sampling distribution.
- It is calculated as the standard deviation of sample means.
**Example:**Let's say you want to estimate the average income of a family in a city.,- calculate the mean income for each sample, and create a sampling distribution of these sample means.
- Using data visualization techniques like
**histograms or box plots**, - you can visualize the distribution of sample means and estimate the population mean income.

### Confidence Intervals

- Sampling distributions are also used to calculate confidence intervals
- which represent the range of values within which the true population parameter (like mean or proportion) is likely to fall.

## Types of data elements

### Numeric Data

- Numeric data consists of
**numerical values**that can be measured and quantified. - Examples include temperature
**readings, sales figures,**and population counts. - A bar chart showing
**monthly sales revenue for a company**, with each bar representing the sales amount in dollars.

### Categorical Data

- Categorical data represents
**qualitative characteristics**and is divided into**distinct categories**or groups. - Examples include product categories, customer segments, and survey responses (e.g., Yes/No).
- A pie chart showing the distribution of product sales by category, where each slice represents a different product category.

### Ordinal Data

- Ordinal data is a type of categorical data that has a
**defined order or ranking.** **Examples**include survey ratings (e.g., satisfaction levels from "Very Satisfied" to "Very Dissatisfied")- and educational levels (e.g., High School, Bachelor's, Master's, etc.).
- A
**horizontal bar chart s**howing the average customer satisfaction ratings for different products, ranked from highest to lowest.

### Time-Series Data

- Time-series data represents
**values**measured over**time intervals.** **Examples**include**stock prices, temperature trends**over months, and website traffic by hour/day.- A line chart depicting the monthly average temperature in a city over the past year,
- with each point representing the temperature for a specific month.

### Text Data

- Text data includes
**unstructured textual information**such as customer reviews**, social media posts**, and email content. - Word clouds or sentiment analysis charts showing the most frequently used words or sentiments in customer reviews for a product or service.

## Conclusion

So we have covered what is sampling and types of sampling such as Random, Stratified, Convenience, Systematic and cluster sampling as well as sampling distribution.