An understanding of statistics is useful whenever you are anaylsing any data and want to take actions based on that. These are some ways how you will use statistics at work:
1. Controlling Process Variations and Stabilizing Processes:
Utilizing statistical methods to manage and stabilize processes by minimizing variations.
2. Determining Appropriate Sample Sizes:
Identifying suitable sample sizes for inspections, test plans, clinical trials, and process validations.
3. Facilitating Financial Decision-Making:
Applying statistical techniques to ensure accurate financial decision-making.
4. Developing Effective Marketing Strategies:
Utilizing statistical insights to formulate appropriate marketing strategies.
You will need statistics whenever you need to convert data into useful information.
In this blog series, we aim to provide insights into fundamental statistical tools. Today’s discussion centres on the definition of data and its various types. Statistics is about data, and a fundamental understanding of data is crucial to manage statistics.
One way of classifying data is by calling them Discrete or Continuous.
Discrete data can be a whole number. Such data can be counted. Some examples of discrete data:
- Number of defective products in a lot
- Number of eggs in a basket
- Number of students in a class
Continuous data can take any value. It is data that can be measured. They can also have decimal numbers. Some examples of continuous data:
- Length of a rod
- Maximum temperature during the day
- Height of children in a classroom
Data can be qualitative or quantitative.
Qualitative data can be Nominal or Ordinal, while Quantitative data can be Interval or Ratio. Also known as ‘Levels of Measurement’, these four categories of data were introduced in 1946 by psychologist Stanley Smith Stevens.
Nominal data is purely descriptive and has no dependency on each other.
Some examples of Nominal data:
- Colours of a rainbow
- Names of birds
Names of birds: An example of ordinal data
Ordinal data is data that has some order or ranking. Examples of Ordinal data:
- Ranking of students in a class from poor, good to excellent
- Levels of income of people like low-level, medium-level and high-level
Interval and ratio are quantitative data.
Interval data is always measured along a scale. Each point is placed at an equal distance from the other. Examples include markings on a scale or time on a watch.
Ratio data measures variables on a continuous scale, with an equal distance between adjacent values. While this is the same feature as in Interval data, Ratio data also has a true zero. Ratio data cannot be negative. Ratio data allows for mathematical operations like multiplication and division. Examples include age in years or the price of a product
You can use Descriptive Statistics for Ratio data. In our upcoming blog, we will delve into the application of Descriptive Statistics for Ratio data.
