G Glossary
 689599.7 rule
 For any bellshaped distribution, approximately 68% of observations lie within one standard deviation of the mean, 95% of observations lie within two standard deviations of the mean, and 99.7% of observations lie within three standard deviations of the mean. Also called the empirical rule.
 Accuracy
 Accuracy refers to how close a sample estimate is to the population value, on average.
 Alternative hypothesis
 The alternative hypothesis proposes that any difference, change or relationship observed in the sample is because a difference, change or relationship exists the population (that is, the difference, change or relationship cannot be explained by sampling variation).
 Bellshaped distributions
 See Normal distributions.
 Bias
 Bias compromises the results or inferences in a study, which may lead to inaccurate conclusions. Bias can be introduced at any part of the research process, and may occur consciously or unconsciously.
 Blinding

Blinding when those involved in the study do not know which comparison group the study individuals are in.
A study can blind the researcher to knowing what comparison group the study individuals are in.
A study can blind the participants to knowing what comparison group they are in.
A study can blind the analysts to knowing what comparison group the individuals are in during analysis.  Blocking
 Blocking is when units of analysis are arranged in groups (called blocks) that are similar to one another.
 Carryover effect
 The carryover effect is when the influence of past experience(s) of the individuals carry over to influence future experience(s) of the individuals, for experimental studies (Sect. 7.3) or observational studies (Sect. 8.3).
 Cases
 The individuals units in the population; the units of analysis. Also called individuals, or subjects when the individuals are people.
 Categorical data
 Cateogorical data is not mathematically numerical data: it consists of categories or labels (even if those labels are numbers). In this book, categorical data is called qualitative data.
 Classical approach to probability
 In the classical approach to probability, the probability of an event occurring is the number of elements of the sample space included in the event, divided by the total number of elements in the sample space, when all outcomes are equally likely.
 Cluster sampling
 A sample where the population is split into a large number of small groups called clusters, then a simple random sample of clusters is selected and every member of the chosen small groups is part of the sample.
 Collusion

Collusion occurs when people work together to produce a work, but only one gets the credit for it.
At university, collusion happens if you give or receive help in completing any form of individual assessment such as assignments and exams.  Comparison
 The comparison in the RQ identifies the small number of different, distinct subsets of the population between which the outcome is being compared. That is, the comparison is between different groups of individuals, or a betweenindividuals comparison. The groups being compared have either imposed differences on the individuals, or have existing differences in the individuals.
The groups being compared have either imposed differences on the individuals, or have existing differences in the individuals.
 Conceptual definition
 A conceptual definition articulates precisely what is being measured, observed or assessed in a study.
 Confidence interval

A confidence interval is an interval in which the population parameter is likely to be contained, if we found many samples the same way.
If we computed the 95% confidence interval (or CI) from each sample, about 95% of the CIs would contain the statistic of interest. This interval is called a confidence interval.
Alternatively, the CI can be seen as the range of plausible values for the parameter that may have produced the observed sample statistic. We studied CIs is some specific situations (there are hundreds more!):
 CIs for one proportion: Chap. 20
 CIs for one mean: Chap. 22
 CIs for a mean difference (paired sample mean): Chap. 23
 CIs for the difference between two means: Chap. 24
 CIs for comparing two odds: Chap. 25
 CIs for regression parameters: Sect. 36.7
 Confounding
 Confounding is when a third variable influences the observed relationship between the response and explanatory variable.
 Confounding variable
 A confounding variable (or a confounder) is an extraneous variable associated with the response and explanatory variables.
 Conditions
 The conditions of interest that those in the observational study can be exposed to.
 Connection
 The connection in the RQ identifies another quantity of interest that varies, that may be related to the outcome.
 Continuous data
 Continuous quantitative data has (at least in theory) an infinite number of possible values between any two given values.
 Control
 A control is a unit of analysis without the treatment applied (but as similar as possible in every other way to other units of analysis).
 Convenience sample
 A sample where individuals are selected because they are convenient for the researcher.
 Data
 Data refers items of information obtained from a study (such as height of seedlings, or the type of medication given).
 Data set
 A data set refers to a collection of data from a study.
 Descriptive study
 A descriptive study is one where the researchers only focus on collecting, measuring, assessing or describing an outcome in the population.
 Discrete data
 Discrete quantitative data has a countable number of possible values between any two given values of the variable.
 Distribution
 The distribution of a quantitative variable describes what values are present in the data, and how often those values appear.
 Ecological validity
 A study is ecologically valid if the study methods, materials and context approximate the real situation being studied.
 Event
 An event is any combination of the elements in the sample space.
 Exclusion criteria
 Exclusion criteria are characteristics that disqualify potential individuals from being included in the study.
 Empirical rule
 For any bellshaped distribution, approximately 68% of observations lie within one standard deviation of the mean, 95% of observations lie within two standard deviations of the mean, and 99.7% of observations lie within three standard deviations of the mean. Also called the 689599.7 rule.
 Experiment (or Experimental study)
 An experimental study (or an experiment) has an intervention: the researchers impose and can manipulate the values of the explanatory variable.
 Experimenter effect
 The experimenter effect is another name for observer bias in experimental studies (that is, when the researchers are unintentionally influenced the subjects).
 Explanatory variable
 An explanatory variable is a variable of interest from the individuals in the study which (potentially) causes changes in, or is related to, the response variable.
 External validity
 Externally validity refers to the ability to generalise the results to other groups in the population apart from the sample studied. For a study to be truly externally valid, the sample must be a random sample from the population.
 Extraneous variable
 An extraneous variable is any variable that is (potentially) associated with the response variable, but is not one the explanatory variable.
 Extrapolation
 Extrapolation refers to making prediction outside the range of the available data. Extrapolation beyond the data can lead to nonsense predictions.
 Fraud
 Fraud refers to the intent to deceive. Fraud can occur by:
 taking an exam for another student or letting someone take an exam for you
 falsifying or inventing research data and findings
 altering or fabricating information
 forging a document
 falsifying past academic records or employment details in order to gain entrance into the university
 Hawthorne effect
 The Hawthorne effect is the tendency of individuals to change their behaviour if they know (or think) they are being observed, in experimental studies (Sect. 7.4) or observational studies (Sect. 8.4).
 Hypothesis
 A hypothesis is a possible answer to a (research) question. More specifically, see null hypothesis or alternative hypothesis
 Hypothesis test
 A hypothesis test is a way to formally answer questions about a population, based on information obtained from a sample. In this book, we have looked at some specific hypothesis tests (hundreds exist: Kanji^{569}!):
 Hypothesis tests about a single mean: Chap. 28
 Hypothesis tests about a mean difference (means of paired samples): Chap. 30
 Hypothesis tests comparing two means: Chap. 31
 Hypothesis tests comparing odds (or percentages): Chap. 32
 Hypothesis tests about a correlation: Sect. 35.4
 Hypothesis tests about regression parameters: Sect. 36.6
 Inclusion criteria
 Inclusion criteria are characteristics that individuals must meet explicitly to be included in the study.
 Independence
 Two events are independent if the probability of one event doesn't change depending on whether or not other event has happened.
 Individuals
 The individuals units in the population from which the observations of interest could be made; the units of analysis. Also called cases, or subjects when the individuals are people.
 Internal validity
 Internally valid refers to how reasonable and logical it is to conclude that changes in the value of the response variable can be attributed to changes in the values of the explanatory variable; that is, the strength of the inferences made from the study. A study with high internal validity has shown that the changes in the response variable can be attributed to changes in the explanatory variables; other explanations have been ruled out.
 Intervention
 An intervention is a comparison or connection whose value can be manipulated by the researchers. That is, the researchers have imposed the intervention upon those in the study to see the change in the outcome.
 IQR
 The IQR is the range in which the middle 50% of the data lie; the difference between the third and the first quartiles.
 IQR rule for identifying outliers
 The IQR rule can identify outliers as either:
 mild (observations \(1.5\times \text{IQR}\) more unusual than \(Q_1\) or \(Q_3\)), or
 extreme (observations \(3\times\text{IQR}\) more unusual than \(Q_1\) or \(Q_3\)).
 Judgement sample
 A sample where individuals are selected, based on the researchers' judgement, depending on whether the researcher thinks they are likely to be agreeable or helpful.
 Levels of a qualitative variable
 The levels (or the values) of a qualitative variable refer to the names of the distinct categories.
 Lurking variable
 A lurking variable is an extraneous variable associated with the response and explanatory variables (that is, is a confounding variable), but whose values are not measured, assessed, described or recorded in the study.
 Mean
 The mean is one way to measure the 'average' value of quantitative data. The arithmetic mean can be considered as the 'balance point' of the data, or the value such that the positive and negative distances from the mean add to zero.
 Median
 The median is one way to measure the 'average' value of some data. The median is a value such that half the values are larger than the median, and half the values are smaller than the median.
 Multistage sampling
 A sample where large groups are selected using a simple random sample, then smaller groups within those large groups are selected using a simple random sample. The simple randomly sampling can continue for as many levels as necessary.
 Nominal variable
 A nominal qualitative variable is a qualitative variable where the levels do not have a natural order.
 Normal distribution
 A normal distribution is symmetrical distribution, with most values in the centre of the distribution. The normal distribution is described by its mean and standard deviation. A picture of a normal distribution is shown in Fig. G.1. Normal distributions are also called bellshaped distributions.
 Null hypothesis
 The null hypothesis proposes that any difference, change or relationship observed in the sample can be explained by sampling variation (that is, no difference, change or relationship exists the population).
 Observational study
 An observational study is one where the researchers do not impose, and cannot manipulate, the comparison or connection upon those in the study to (potentially) change the response of the participants.
 Observer effect
 The observer effect occurs when the researchers (unconsciously) change their behaviour to conform to expectations because they know what values of the explanatory variable apply to the individuals. This may cause the individuals to change their behaviour or reporting also.
 Odds
 The odds of some event is the proportion (or percentage, or number) of times that an event happens, divided by the proportion (or percentage, or number) of times that the event does not happen.
 Odds ratio
 The odds ratio is how many times greater the odds of an event are in one group, compared to the odds of the same event in another group.
 Operational definition
 An operational definition articulates exactly how something will be identified, measured, observed or assessed.
 Ordinal variable
 An ordinal qualitative variable is a qualitative variable where the levels do have a natural order.
 Outcome
 The outcome in a RQ is the result, output, consequence or effect of interest in a study, numerically summarising the entire population (or subsets of the population).
 \(P\)value
 A \(P\)value is the likelihood of observing the sample results (or something even more extreme) over repeated sampling, under the assumption that the null hypothesis about the population is true.
 Parameter
 A parameter is a number describing some feature of a population, and is usually estimated by a statistic.
 Paired data
 Paired data is when two observations about the same variable are recorded for each unit of analysis.
 Percentage
 A percentage is a proportion, multiplied by 100. In this context, percentages are numbers between 0% and 100%.
 Percentiles
 The \(p\)th percentile of the data is a value separating the smallest \(p\) of the data from the rest.
 Placebo
 A placebo is a treatment with no intended effect or active ingredient.
 Placebo effect
 The placebo effect is when individuals report perceived or actual effects without having received the treatment or condition, in experimental studies (Sect. 8.5) or observational studies (Sect. 8.5).
 Plagiarism

Plagiarism is using other people’s ideas and research to develop new conclusions, or confirm existing conclusion.
All sources used when writing research should be acknowledged, otherwise you are committing plagiarism.
Plagiarism can be deliberate or accidental:
 Deliberatefor instance, if a student intentionally copies the work of others and pretends it is their own work.
 Accidentalfor instance, if a student has poor notetaking skills or doesn't know how to reference correctly, and they inadvertently present someone else's ideas and words as their own.
 Population
 The population is the group of individuals (or cases, or subjects if the individuals are people) from which the total set of observations of interest could be made, and to which the results will (hopefully) generalise.
 Precision
 Precision refers to how likely it is that the sample values will be similar or close together, and not vary much from sample to sample.
 Proportion
 A proportion is a fraction out of a total. Proportions are numbers between 0 and 1.
 Protocol
 A protocol is a predefined procedure detailing the design and implementation of studies, and for data collection.
 Qualitative data
 Qualitative data is not mathematically numerical data: it consists of categories or labels (even if those labels are numbers). Also called categorical data.
 Quantitative data
 Quantitative data is mathematically numerical data: the numbers themselves have numerical meaning, and it makes sense to be able to perform mathematical operation on them. Most data that are counted or measured will be quantitative. Also called scale data.
 Quantitative research
 Quantitative research summarises and analyses data using numerical methods, such as producing averages and percentages.
 Quartiles
 Quartiles describe the variation and shape of data:
 The first quartile \(Q_1\): A value that separates the smallest 25% of observations from the largest 75%. The \(Q_1\) is like the median of the smaller half of the data, halfway between the minimum value and the median.
 The second quartile \(Q_2\): A value that separates the smallest 50% of observations from the largest 50%. (This is the median.)
 The third quartile \(Q_3\): The value that separates the smallest 75% of observations from the largest 25%.
 The \(Q_3\) is like the median of the larger half of the data, halfway between the median and the maximum value.
 Quasiexperiment
 In a quasiexperiment, the researchers (1) allocate treatments to groups of individuals (i.e., do not allocate the values of the explanatory variable for the individuals), but (2) do not determine who or what is in those groups.
 Random
 In research and statistics, random means "determined completely by chance".
 Range
 The range is the maximum value of a variable minus the minimum value of the variable.
 Relative frequency approach to probability
 In the relative frequency approach to probability, the probability of an event is (approximately) the number of times the outcomes of interest has appeared in the past, divided by the number of 'attempts' in the past.
 Representative samples
 A representative sample is one where the individuals in the sample are not likely to be different the individuals not in the sample, at least for the variables of interest.
 Response variable
 A response variable is the variable used to measure, assess or describe the outcome on each individual in the population.
 Sample
 A sample is a subset of the population of interest which is actually studied, and from which information is collected.
 Sample space
 The sample space is a list of all possible and distinct results after administering a procedure whose result is unknown beforehand. is a list of the results after administering a procedure whose result is unknown beforehand.
 Sampling distribution
 A sampling distribution is the distribution of some sample statistic, showing how its value varies from one sample to sample.
 Sampling frame
 The sampling frame is a list of all the members of the population (the individuals, or cases, or subjects).
 Sampling variation
 Sampling variation refers to how much a sample estimate (a statistic) is likely to vary from sample to sample, because each sample is different.
 Selection bias
 Selection bias is the tendency of a sample to over or underestimate a population quantity.
 Scale data
 Scale data is mathematically numerical data: the numbers themselves have numerical meaning, and it makes sense to be able to perform mathematical operation on them. Most data that are counted or measured will be quantitative. In this book, scale data is called quantitative data.
 Simple random sample
 A sample where every possible sample of the same size has same chance of being selected.
 Standard deviation
 The standard deviation is, approximately, the average distance that observations are away from the mean.
 Standard deviation rule for identifying outliers
 For approximately symmetric distributions, any observation more than three standard deviations from the mean can be considered an outlier.
 Standard error
 A standard error is the standard deviation of all possible values of the sample estimate (from samples of a certain size). Any quantity estimated from a sample has a standard error.
 Stratified sampling
 A sample where the population is split into a small number of large (usually homogeneous) groups called strata, then cases are selected using a simple random sample from each stratum.
 Statistic
 A statistic is a number describing some feature of a sample (to estimate a population parameter).
 Statistical validity
 A result is statistically valid if the conditions for the underlying mathematical calculations and assumptions to be approximately correct are met. Every confidence interval and hypothesis test has statistical validity conditions.
 Subjective approach to probability
 In the subjective approach to probability, various factors are incorporated, perhaps subjectively, to determine the probability of an event.
 Subjects
 The individuals units in the population when they are people; the units of analysis. Also called individuals or cases; however, those two terms do not refer to people.
 Systematic sampling
 A sample where the first case is randomly selected; then, every \(n\)th individual is selected.
 Treatments
 Treatments are the conditions of interest that those in the study can be exposed to (as the explanatory variable). In experiments, treatments are imposed by researchers.
 True experiment
 In a true experiment, the researchers (1) allocate treatments to groups of individuals (i.e., allocate the values of the explanatory variable for the individuals), and and (2) determine who or what is in those groups.
 Unit of observation
 The unit of observation is the 'who' or 'what' which are observed, from which measurements are taken and data collected.
 Unit of analysis
 The smallest collection of units of observations (and perhaps the units of observations themselves) about which generalizations and conclusions are made; the smallest independent 'who' or 'what' for which information is analysed. Units of analysis should not typically share a common underlying source. In an experimental study, the unit of analysis is the smallest collection of units of observations that can be randomly allocated to separate treatments.
 Unstandardizing formula
 When the \(z\)score is known, the unstandardising formula determines the corresponding value of the observation \(x\).
 Values of a qualitative variable
 The levels (or the values) of a qualitative variable refer to the names of the distinct categories.
 Variable
 A variable is a single aspect or characteristic measured, assessed, described or recorded from or about individuals, that can vary from individual to individual.
 Voluntary response (selfselecting) sample
 A sample where individuals participate if they wish to.
 \(z\)score
 A \(z\)score measure how many standard deviations a value is from the mean. In symbols: \[ z = \frac{x  \mu}{\sigma}, \] where \(x\) is the value, \(\mu\) is the mean of the distribution, and \(\sigma\) is the standard deviation of the distribution.