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Statistics 101: Principles of Statistics11 chapters | 144 lessons | 9 flashcard sets

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Lesson Transcript

Instructor:
*Cathryn Jackson*

Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

You might be surprised to find that data is more than just a collection of numbers. Data is divided into several categories, including discrete and continuous data. Find out why!

Madison is doing some fundraising for her school band. When the fundraising is finished, she will need to create a report that identifies how many products she sold, how much money she raised, and how many instruments they can buy using the money Madison raised. Each of these items represents different types of data.

In this lesson, you will learn about the different types of numerical, or quantitative, data. There are two types of numerical data: discrete and continuous. However, before we get into the different types of numerical data, it is important to understand data and numerical data.

First, you need to understand **data**, which is information that is collected for analysis. Data is generally used to prove or disprove a hypothesis. Data can be grouped into two different categories: categorical and quantitative. You can learn more about the differences between categorical and quantitative data in our other lessons. For now, understand that categorical data is grouped into categories or topics, while **quantitative data** is data that can be measured and ordered.

As part of her fundraiser, Madison is selling cupcakes door-to-door in an effort to buy more flutes and trumpets for the school band. Instruments and cupcakes represent a form of quantitative data known as **discrete data**, which is data that cannot be divided; it is distinct and can only occur in certain values. For example, instruments and cupcakes are both discrete data in this scenario because Madison cannot buy half of an instrument or sell half of a cupcake. The value of cupcakes and instruments are distinct values.

On the other hand, **continuous data** is data that can be divided infinitely; it does not have any value distinction. The amount of time it takes to sell the cupcakes is an example of continuous data. You can remember this by remembering that time itself is continuous and never ending. Also, you can divide time infinitely. There are centuries, decades, years, months, weeks, days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds and so on.

Now that you understand the differences between continuous data and discrete data, let's practice identifying the two.

Madison and her friend Charlotte are baking more cupcakes to sell in the fundraiser. They are having a competition to see whose cupcake is the tallest (icing included) and whose cupcake has the most sprinkles. Which piece of data is discrete and which is continuous? Pause the video here to work on your answer.

Did you get it? Madison and Charlotte are measuring the number of sprinkles on the cupcakes and the height of the cupcakes. While the height of the cupcakes can be divided infinitely, the number of sprinkles on the cupcake cannot. Therefore, the sprinkles are discrete data and the height of the cupcakes are continuous.

When collecting data, it is important to understand which type of data you are dealing with in each situation. Remember, there are two types of data: categorical and quantitative. In this lesson, we talked about the different types of quantitative data: discrete and continuous. **Discrete data** is data that cannot be divided; it is distinct and can only occur in certain values such as the instruments, cupcakes, and sprinkles. **Continuous data** is data that can be divided infinitely; it does not have any value distinction, such as time, height, and weight.

Don't forget! Discrete data has a specific value, while continuous data can be divided infinitely.

Once you are finished with this lesson, you should be able to identify and describe the two types of quantitative data.

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Statistics 101: Principles of Statistics11 chapters | 144 lessons | 9 flashcard sets

- Descriptive & Inferential Statistics: Definition, Differences & Examples 5:11
- Difference between Populations & Samples in Statistics 3:24
- Defining the Difference between Parameters & Statistics 5:18
- Estimating a Parameter from Sample Data: Process & Examples 7:46
- What is Quantitative Data? - Definition & Examples 4:11
- What is Categorical Data? - Definition & Examples 5:25
- Discrete & Continuous Data: Definition & Examples 3:32
- The Purpose of Statistical Models 10:20
- Experiments vs Observational Studies: Definition, Differences & Examples 6:21
- Random Selection & Random Allocation: Differences, Benefits & Examples 6:13
- Convenience Sampling in Statistics: Definition & Limitations 6:27
- How Randomized Experiments Are Designed 8:21
- Analyzing & Interpreting the Results of Randomized Experiments 4:46
- Confounding & Bias in Statistics: Definition & Examples 3:59
- Confounding Variables in Statistics: Definition & Examples 5:20
- Bias in Statistics: Definition & Examples 7:24
- Bias in Polls & Surveys: Definition, Common Sources & Examples 4:36
- Misleading Uses of Statistics 8:14
- Go to Overview of Statistics

- Go to Probability

- Go to Sampling

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