Introduction to Types of Statistics
In this discussion, we will continue exploring statistics, focusing on the different types of statistics. This topic is important, especially for interviews, where you might be asked to explain the two different types of statistics with examples.
Two Main Types of Statistics
Usually, statistics is divided into two types:
- Descriptive Statistics
- Inferential Statistics
Descriptive Statistics
Descriptive statistics consists of organizing and summarizing all data. All techniques specifically used to organize and summarize data belong to descriptive statistics.
Some basic techniques used in descriptive statistics include:
- Measure of Central Tendency
- Measure of Dispersion
Measure of Central Tendency
This includes techniques like:
- Mean
- Median
- Mode
These are common measures that many are familiar with.
Measure of Dispersion
This includes techniques such as:
- Variance
- Standard Deviation
These techniques help summarize the spread or variability of the data.
The standard deviation is often represented by specific symbols, which will be discussed in detail later. As we proceed through this series of statistics videos, you will gain a deeper understanding of these topics.
Inferential Statistics
Inferential statistics deals with making conclusions or inferences from collected data. After collecting data, we perform experiments to draw conclusions about a larger group.
Some of the experiments used in inferential statistics include tests like the Z-test. We will learn about these tests in detail as we progress.
The process involves collecting sample data, performing experiments, and making conclusions or inferences about the population data, which is generally larger than the sample data.
We take some sample data, perform experiments, and then draw conclusions about the population data. The population data size is always greater than the sample data size.
Example to Illustrate Descriptive and Inferential Statistics
Suppose there is a college named A with many students, say 1000 students. Within this college, there is a statistics class where we randomly pick some students’ heights as sample data.
For example, the heights might be 180 cm, 170 cm, 162 cm, 150 cm, 160 cm, and so on. This is the sample data collected from the students.
Descriptive Statistics Application
Using descriptive statistics, we might calculate the mean, median, or mode of this sample data. For instance, the mean height of this sample could be approximately 165 cm. This gives us a summary of the sample data.
Inferential Statistics Application
Using inferential statistics, we try to infer or conclude what the average height might be for all 1000 students in the college based on the sample data. This involves making inferences about the population from the sample.
This is a fundamental difference between descriptive and inferential statistics. Descriptive statistics summarizes the sample data, while inferential statistics uses the sample data to make conclusions about the population.
As we continue, we will discuss in depth topics such as:
- What is population?
- What is sample?
- Measures of central tendency
- Measures of dispersion
These concepts will be explored further in upcoming sessions.
Conclusion
This session provided a basic understanding of the two types of statistics: descriptive and inferential. We also introduced key concepts and examples to illustrate their differences and applications.
Key Takeaways
- Statistics is broadly divided into two types: descriptive and inferential statistics.
- Descriptive statistics involves organizing and summarizing data using techniques like measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation).
- Inferential statistics involves making conclusions or inferences about a population based on sample data through various experiments.
- Understanding the difference between population and sample data is crucial for applying inferential statistics effectively.