Definitions of Statistics and Types of Statistics

Imagine you surveyed 100 customers about their favourite ice cream flavor.

The result is: Chocolate: 50 votes , Vanilla: 30 votes, Strawberry: 15 votes

Now, you want to know:

1. How many people voted for chocolate?
2. Average no. of votes per flavor
3. Which flavor got the most votes?
4. The difference between the highest and lowest no. of votes
5. Create charts for visualizing frequency.

and many other different patterns you can find from the dataset. This process is known as statistics.

Hence, we can say, statistics is a way of using numbers to find patterns from the data. It’s like taking a bunch of information, organizing it, and figuring out what it all means so we can understand what is happening in the world to make better decisions.

Two main terms associated with Statistics are: Population, Sample.

Population in statistics refers to the entire group of individuals or items that you want to study or draw conclusions about. It includes every member of a defined group. In the above example, total 100 people are the population.

Sample in statistics is a smaller group selected from the population. It is used to represent the population in studies because it is often impractical or impossible to study the entire population. By studying a sample, we can make inferences about the population.

Types of Statistics:

Statistics can be broadly divided into two main categories:

1. Descriptive Statistics
2. Inferential Statistics

1. Descriptive Statistics

Descriptive statistics involves methods for summarizing and organizing data in an informative way. It helps us take a lot of information and turn it into simple summaries.

Key Components:
1. Measures of Central Tendency: Mean, Median, Mode

2. Measures of Dispersion: Range, Variance, Standard Deviation

3. Measures of Shape: Skewness, Kurtosis

4. Frequency Distribution: Histogram

5. Measures of Position: Quantiles, Percentiles

6. Summary Tables: Count, Sum

2. Inferential Statistics

Inferential statistics make predictions or guesses based on a sample data.

Key components:
Estimation: Point Estimation, Interval Estimation

Hypothesis Testing: Null Hypothesis (H0), Alternative Hypothesis (H1), P-Value, Significance Level (α)

Regression Analysis: Simple Linear Regression, Multiple Regression

ANOVA (Analysis of Variance)

These two broad categories of statistics help researchers and analysts to understand data, derive insights, make predictions, and support decision-making processes.