Elements are the entities on which the data are collected.

A variable is a characteristic of interest about an element. A characteristic that can take on different possible outcomes is called a variable.

The variable is defined as quantitative if the outcomes are numbers.

The variable is defined as qualitative if the outcomes are nonnumeric qualities or attributes.

The outcomes obtained on all variables for one element in the data set is called a case.

The outcome about a single variable for an element in the data set is called an observation.

The assignment of numerals has two basic rules:
1. Numerals are labels that have no inherent meaning, for example zip codes, automobile license plates, or student identification number.
2. Numerals are numbers that have quantitative meaning and can be analyzed, for example, age, height, weight or weekly wage of a worker.

Four different scales for measurement have been identified. These are nominal, ordinal, interval and ratio scales.

Interval scale because interval scales of measurement have the properties of classifying, magnitude, and equal intervals. While the ordinal scales of measurement show that individuals have more or less something than the others, interval scales have more precise information indicating how much of something individuals have. 

Test scores would be dependent as they are the ones that cannot be labelled or the ones that are wanted to be examined. 

Open-ended questions allow individuals to give a free-form answer. However, closed-ended questions can be answered with “Yes” or “No,” or they have a limited set of possible answers such as “A”, “B”, “C”, or “all of them”.

It would require a single response since the question has the word favourite in it.

Once a questionnaire has been developed, it is important to determine whether respondents are interpreting questions as intended and whether the order of questions may influence responses. For this purpose, a pretest is conducted using small sample of the survey population. Pretesting finds out the unpredictable difficulties in the layout of the questionnaire and in the clarity of wording. Hence, the problems determined in the pretesting can then be corrected before the research is begun.

Error in population specification as seniors are not the only students at a university. There are also freshmen, sophomore, and junior students. 

Error in measurement can be described as any discrepancy between the actual result obtained and the correct result that would be provided by an ideal procedure. From a statistical point of view any observation is composed of the true value plus some random error value. However, all error is not random. The error component of any observation can be divided into two subcomponents, random error and systematic error.

Random errors are caused by unknown and unpredictable factors that randomly affect measurement of the variable across the sample. Random error does not have any consistent effects across the entire sample. Instead, it affects observed scores up or down randomly. Random error adds variability to the data and it is sometimes called noise.

Systematic errors are reproducible inaccuracies that shift measurements from their true value by the same amount and consistently in the same direction. This type error arises from problematic, poor calibrated or incorrectly used equipment.

Raw data or primary data, which are collected directly related to their object of study, are the original measured values or scores, without any manipulation, except perhaps sorting in the case of quantitative data.

To construct a frequency distribution table, the measurement categories and the number of responses within a given measurement category are used.

When the data set is large or the measurements are obtained using ratio scale, grouped frequency is more appropriate for summarizing the data.