Terminology

Statistics helps us make sense of information by collecting, organising, and analysing data. In Junior Cycle Maths, you will learn how to identify different types of data, collect data accurately, and analyse it to draw conclusions.

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1. Types of Data: Understanding the Different Forms

In this section, you will learn about the different types of data you may encounter in statistics. Knowing the difference between data types is important because it helps you decide how to collect, organise, and analyse the information effectively.

Numerical Data:

• What it is: Numerical data refers to data that consists of numbers. These numbers can represent counts or measurements and are often used when you need to perform mathematical calculations. Numerical data can be further classified as either discrete or continuous.

Categorical Data:

• What it is: Categorical data consists of names, labels, or categories. This type of data is not about numbers but rather about grouping items based on characteristics or qualities. You cannot perform mathematical operations on categorical data.

Discrete Data:

• What it is: Discrete data is a type of numerical data that can only take specific values, often whole numbers. These values are distinct and separate, meaning there are no in-between values. Discrete data usually involves counting.

Continuous Data:

• What it is: Continuous data is another type of numerical data, but unlike discrete data, it can take any value within a range. This means it can include decimals and fractions. Continuous data often comes from measuring something, where the value can be infinitely precise.

Ordinal Data:

• What it is: Ordinal data is a type of categorical data where the order of the categories matters. In other words, the categories can be ranked or arranged in a meaningful sequence. However, the difference between each category is not necessarily equal.

Nominal Data:

• What it is: Nominal data is a type of categorical data where the order of the categories does not matter. The categories are simply different from each other with no inherent ranking. This type of data is often used for labelling variables without any quantitative value.

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2. Collecting Data: Gathering Information Correctly

This section focuses on the different methods of collecting data. How you collect data can affect the quality and accuracy of your results. You will also learn about the difference between collecting data from a whole group or just a sample.

Primary Data:

• What it is: Primary data is data that you collect firsthand. It involves going out and gathering information directly from sources like surveys, experiments, or observations. This type of data is fresh and specific to your needs but can be time-consuming to collect.

Secondary Data:

• What it is: Secondary data is data that has already been collected by someone else. This data is usually found in books, reports, or on the internet. While it's easier to access, secondary data might not perfectly fit your specific needs because it wasn't collected with your particular study in mind.

Population:

• What it is: A population is the entire group of people or items you are interested in studying. When you collect data from a population, you're aiming to understand something about every member of that group.

Sample:

• What it is: A sample is a smaller group selected from the population. You use a sample when it's impractical or impossible to gather data from the entire population. The goal is to choose a sample that accurately represents the larger population.

Sampling Frame:

• What it is: A sampling frame is the list of all the possible members of the population that you could potentially include in your sample. It serves as a guide for choosing who or what to sample.

Census:

• What it is: A census is a method of data collection that involves gathering information from every single member of the population. While very thorough, conducting a census can be time-consuming and expensive.

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3. Analysing Data: Making Sense of the Information

Once you have collected your data, the next step is to analyse it. In this section, you will learn about key concepts such as outliers and bias, which can influence how you interpret your data.

Outlier:

• What it is: An outlier is a data point that is significantly different from the other data points in your dataset. It stands out because it is much higher or lower than the rest of the values. Outliers can affect the average (mean) and sometimes distort the overall picture of the data.

Bias:

• What it is: Bias occurs when the data you collect does not fairly represent the population. This can happen due to the way the data is collected, who is chosen to participate, or how questions are asked. Bias can lead to misleading conclusions because the data doesn't accurately reflect the whole population.

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