Data Types

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#18663

Data Types

Quantitative or Numerical Data: data that has a numerical value. It is counting something and the answer must be a number. How many people drive to school, how many children in a family etc. This is sometimes also called Numerical data and can be split into two types:

Continuous: the value can be anything within a specified range. If the number can be “in between” numbers, it doesn’t have to be one number or the next, then it is said to be continuous. For example height is continuous since you don’t have to be 150cm or 151cm, you can be 150.453 cm. Height, weight, time, speed are all examples of continuous data.

Discrete: numerical data that has to be a certain amount, it cannot be in between the jumps. For example, shoe sizes must be size 6, or 6½, or 7 or 7½ etc but it cannot be anything other than those predefined values. In general terms, anything that has to be a whole number (or in the case of our shoe size, half as well). The values must always differ by a fixed amount.

Categorical Data: data that describes a characteristic, that fits into a category. It is not a number, it is more like a word answer. For example, favourite colour, favourite food, TV show. Sometimes it is also called Qualitative data, that is it has a quality or type, not an amount. It can also be defined as Nominal Data (ie we nominate a category or description for it)

Census: A census is when the whole target group is sampled. For example, if you took a census of Year 12 students at your school, every single student would have to be included. The advantages of a census is that the information is very accurate. The primary disadvantage is how much time, effort and cost it takes to ask large populations groups.

When a census is impractical, which is most of the time, we take a Sample of the population and then make conclusions based on that.

Sample: A sample is when a small number of people are chosen to represent the larger population, and then conclusions can be made regarding the whole population. The advantage of a sample is it is cheap and much more efficient and easy to produce. The primary disadvantage is if your sample isn’t chosen wisely, the results can be biased and not at all representative.

Samples can be biased, which leads to ‘tainted’ data, and incorrect conclusions.

Biased Sampling: is when a sample is taken in a way that does not yield unbiased results.
An example of a biased sample could be a question regarding attitudes towards smoking. If you asked 100 people in a shopping centre, right in front of the entrance to a tobacconist, then it is most likely the patrons would all be pro-smoking as they are probably in the tobacconist to purchase smoke. Your sample is biased towards smoking.
Another example may be what is the most popular type of car. Say we go to Easter Creek Drags on a Wednesday nigh to count the number of each type of car, this may seem representative as there are many cars there. It would be biased however since the type of person who goes to a drag race is more than likely to have more interest in their own car, probably driving a ‘hot’ car. You wouldn’t see too many old drivers in ‘safer’ and more ‘boring’ cars.

Random Sample: A random sample is where each member of the target population has an equal chance of being selected. There are a number of ways to select a sample randomly. Tables are used of random numbers, assigning each member a number and choosing them based on the number picked. Putting names in a hat and drawing them out.

Stratified Sample: A stratified sample is when there is more than one group to pick from and you pick the number based on the fraction or proportion in the population. For example, if a school had 300 boys and 100 girls and there was a sample of 100. Since three-quarters of the school are boys, then three-quarters (75) of the sample should be boys, and only one-quarter girls.

Systematic Sample: When a ‘system’ is used to select the sample. Such as selected every tenth person in the line, or choosing people with blue eyes. Systematic samples can lead to unstable results if not chosen with care.

Primary Source: when you have collected the data yourself directly from the sample or census population

Secondary Source: when the data has been collected by another agent or person on your behalf

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#18665

A car manufacturer keeps records as to the most popular colour of cars that are sold. i. Which of the following best describes the type of data collected a. Discrete b. Stratified c. Categorical d. Continuous
C	Categorical because the colour of a car is a category, then the data is Categorical
ii. The type of survey conducted by the car manufacturer could best be described as: a. random sample b. stratified sample c. systematic sample d. census
D	census because they kept records of all the cars sold, that makes it a census (not a sample of only a few of the cars)

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#18666

The SRC is trying to determine whether the student body would support a change to sell only healthy food at the canteen. They decide to do a stratified random sample. This would best be done by: a. surveying students randomly as they enter the canteen b. surveying an equal number of students from each year c. selecting 50 students’ names from a hat d. selecting a number of students from each year, in proportions that reflect that year’s proportion of the school body

D

a stratified sample means that you select the same proportion of people in the sample that is in the population, to more accurately represent the population in your sample

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