Statistical terms

Retrospective study

(also called case-control) is where data are collected from the past by going back in time (through examination of records, interviews, and so on).

Random Sample

A Random Sample is a sample that allows each member of a population to have an equal probability of being selected. An example would be a lottery.

Parameter

A parameter is a numerical measurement of a population. An example of a parameter is there are exactly 100 Senators in the 109th Congress of the United States, and 55% of them are Republicans. The figure 55% is a parameter because it is based on the entire population of Senators.

Population

A population is the complete collection of all individuals to be studied. For example,if you were conducting some type of survey, the people you ask to participate in the survey would be considered the population.

Sample

A sample is a subcollection of members selected from a population!For example, if a scientist wanted to study the effects of a new drug on men and women between the ages of twenty and twenty-five then he would choose a small sample of twenty to twenty-five year olds in the area.

Simple Random Sample

A simple random sample is a sample in which every member of the population has an equal chance of being chosen AND each group of members has an equal chance. An example of a simple random sample would be a group of 25 employees chosen out of a hat from a company of 250 employees.

Statistic

A statistic a numerical measurement describing some characteristic of a sample. An example of a statistic is Readers Digest polled 23 million voters in the United States and, 78% said they would be voting in the next presidential electiuon. The figure 78% is a statistic.

Voluntary Response Sample

A voluntary response sample is a sample that involves only those who choose to participate. For example, a new television show might ask viewers to participate in an online poll or mail-out survey. This would be a voluntary sample.

Experiment

An experiment, which we apply some treatment and then proceed to observe its effects on the subjects. An example of this would be In the largest public health experiment ever conducted 200,745 children were given a treatment consisting of Salk vaccine, while 201,229 other children were given a placebo. The Salk vaccine injections constitute a treatment that modified the subjects.

Categorical Data

Categorical Data (or qualitative data) consists of names and labels that do not represent numbers or measurements. An example of categorical data could be political party affiliations such as Republicans or Democrats.

Confounding

Confounding occurs in an experiment when you are not able to distinguish among the effects of different factors. Try to plan the experiment so that confounding does not occur.

Ordinal Level of Measurement

Data can be arranged in some type of order, but differences between the data values either cannot be calculated or are meaningless. For example, the ranks of 1st, 2nd, 3rd, or 4th given at a contest, game, etc. represent a method of ordering. The difference between the actual numbers does not represent an exact quantity related to the ranks.

Data

Data is collections of observations such as measurements, genders, and survey responses.An example of data could be measuring distance of a ball being thrown in feet, doing it more than one time!

Continuous data

Data that can take any value within a range, not just certain fixed values. An example of continuous data is the heights of people.

Discrete Data

Discrete Data results when the number of possible values is either an finite number or a countable number. The possible values could be 0, 1, 2, etc... An example of discrete data would be the number of eggs a hen would lay at a time. This type of data represent counts.

Systematic sample

In this type of sampling, we select some starting point and then select every kth (such as every 10th) element in the population.

Nominal Level of Measurement

Nominal level of measurement is characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme. An example of Nominal level of measurement is survey responses such as yes, no, and undecided.

Quantitative Data

Quantitative data is data that is identified or measured on a numeric scale. Quantitative data is usually found using statistical methods. For example, if you asked people to tell you how many siblings they have, that is quantitive data.

Observational Study

Research or an experiment in which the researcher does not manipulate the groups being studied. The researcher develops conclusions based on comparisons of one group and a control group. An example of an observational study would be a doctor observing the development/risks of developing lung cancer in smokers versus non-smokers.

Interval level of measurement

This is like the ordinal level where data can be arranged in some order but differences obtained by subtraction are meaningless, with the additional property that the difference between any two data values is meaningful. However, data at this level do not have a NATURAL zero starting point. Interval scales provide information about order, and also possess equal intervals. An example of an interval scale is temperature, either measured on a Fahrenheit or Celsius scale. A degree represents the same underlying amount of heat, regardless of where it occurs on the scale. Measured in Fahrenheit units, the difference between a temperature of 46 and 42 is the same as the difference between 72 and 68.

Sampling error

This type of error is the difference between a sample result and the true population result; such and error results from chance sample fluctuations. For instance, if you flip a fair coin, you would probably not get a result of 50% heads and 50% tails. You might get a result of 40% heads and 60% tails. This is the sampling error.

Non-sampling error

This type of error occurs when the sample data are incorrectly collected, recorded, or analyzed. (such as by selecting a biased sample, using a defective measurement instrument, or copying the data incorrectly.)

Cluster Sample

When we divide the population area into sections (or clusters) , then randomly select some of those clusters and then choose all the members from those selected clusters.

Cross-sectional study

Where data are observed, measured, and collected at one point in time.

Convenience Sample

Where we simply use results that are very easy to get.

Stratified Sample

With this type of sampling, we subdivide the population into at least two different subgroups (or strata) so that subjects within the same subgroup share the same characteristics (such as gender or age bracket), then we draw a sample from each subgroup (or stratum)

Prospective Study

data are collected in the future from groups sharing common factors (called cohorts). These are also called longitudinal or cohort studies.

Ratio level of measurement

the interval level with the additional property that there is also a natural zero starting point (where zero indicates that NONE of the quantity is present). For values at this level, differences and ratios are both meaningful. In addition to possessing the qualities of nominal, ordinal, and interval scales, a ratio scale has an absolute zero (a point where none of the quality being measured exists). Using a ratio scale permits comparisons such as being twice as high, or one-half as much. Most measurement in the physical sciences and engineering is done on ratio scales. Mass, length, time, plane angle, energy and electric charge are examples of physical measures that are ratio scales.