Statistics – the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data. Population – the collection of all elements to be studied. Census – a collection of data from every member of the population Sample – a subset of members selected from a population
Vocabulary 1 – Chapter 1 Section 2
Statistical Significance – an arbitrary limit where an observed difference is reasonably assumed to be due to some factor other than pure chance. Practical significance – an arbitrary limit where an observed difference is of some practical use in the real world. Vocabulary 1 – Chapter 1 Section 3
Parameter – a numerical measurement of a population characteristic. Statistic – a numerical measurement of a sample characteristic. Quantitative Data – data that consists of numbers representing counts of measurements. Categorical Data – data that can be separated into different categories that are distinguished by some nonnumeric characteristic.
Discrete Data – data that has a finite number or countable number of possible values. Continuous Data – data that has an infinite number of possible values, with no gaps in the possible values. Nominal Level of Measurement – data that are qualitative only. Ordinal Level of Measurement – data that can be ordered, but computational differences are meaningless. Interval Level of Measurement – data that are ordinal level, and also having meaningful computational differences, but having no significant zero value.
The Homework on Common Levels of Data Measurement
Four common levels of data measurement follow. •Nominal Level. The lowest level of data measurement is the nominal level. Numbers representing nominal level data (the word level often is omitted) can be used only to classify or categorize. Employee identification numbers are an example of nominal data. The numbers are used only to differentiate employees and not to make a value statement about ...
Ratio Level of Measurement – data that are interval level, and also having meaningful computational differences, and having a significant zero value. Vocabulary 1 – Chapter 1 Section 4
Voluntary Response Sampling – a style of sampling that incorporates non probability sampling methods in its research. Correlation – the degree to which two or more attributes or measurements on the same group of elements show a tendency to vary together.
Causality – the principle that nothing can happen without being caused. Order of Questions – sometimes survey questions are unintentionally loaded by factors of the items being considered. Nonresponse – occurs when someone either refuses to respond to a survey question or is unavailable. Missing Data – occurs when no data value is stored for the variable in an observation. Vocabulary 1 – Chapter 1 Section 5
Observational Study – observe and gather data without attempting to modify the subjects. Experiment – apply some treatment and then observe the effects of the treatment on the subjects. Simple Random Sample – a sample of n subjects that is selected in a way that makes every possible sample of size n is equally likely. Random Sample – a sample in which each individual member of a population is equally likely to be chosen. Probability Sample – a sample in which each individual member of a population has a known chance of being chosen.
Systematic Sample – a sample selected by choosing a starting point in the list of subjects and then selecting every kth subject from that point on. Convenience Sample – a sample selected by choosing subjects that are most easily accessed. Stratified Sample – a sample selected by dividing the population into at least two subgroups and then choosing subjects from each subgroup. Cluster Sample – a sample selected by dividing the population into at least two subgroups, randomly selecting subgroups, and then choosing all subjects from the selected subgroups. Sampling Error – error in a statistical analysis arising from the unrepresentativeness of the sample taken.
