D Glossary
 \(68\)\(95\)\(99.7\) 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 likely to be 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.
 Betweenindividual comparisons
 See Comparison (between individuals), Comparison.
 Bias
 Bias refers to any systematic misrepresentation of the target population by the sample.
 Blinding

Blinding when those involved in the study do not know which comparison group the individuals are in.
A study can blind the researcher to knowing what comparison group the 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 occurs when units of analysis are arranged or analysed as separate groups of similar units (called blocks).
 Carryover effect
 The carryover effect occurs when the influence of one treatment or conditions influences individuals' responses to subsequent treatments or conditions. (Sect. 7.6).
 Cases
 The individuals units in the population; the units of analysis. Also called individuals, or subjects when the individuals are people.
 Categorical data
 See qualitative data.
 Chisquare (\(\chi^2\)) score
 The teststatistic used when comparing odds (or proportions). The \(\chi^2\) statistic measures the overall size of the differences between the expected counts and observed counts, over the entire \(2\times 2\) table.
 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.
 Comparison
 In a RQ, a comparison refers to groups of individuals being compared. The comparison may be within individuals, or between groups of individuals. Also see Comparison (between individuals), Comparison (within individuals).
 Comparison (between individuals)
 The betweenindividuals comparison in the RQ identifies the small number of groups of different individuals for which the outcome is compared. Also see Comparison (within individuals), Comparison.
 Comparison (within individuals)
 The withinindividuals comparison in the RQ identifies the small number of different, distinct situations for which the outcome is compared for each individual. Also see Comparison (between individuals), Comparison.
 Compound event
 A compound event is any combination of simple events (i.e., of elements in the sample space).
 Conceptual definition
 A conceptual definition articulates precisely what words or phrases mean in a study.
 Conditions
 The conditions are the values of the comparison that those in the observational study experience, but are not imposed by the researchers.
 Confidence interval
 Informally: a confidence interval (CI) is an interval likely to contain the unknown value of the parameter. More formally, a CI is an interval which contains the unknown parameter a given percentage of the time (over repeated sampling). We studied CIs in some specific situations (there are hundreds more!):
 CIs for one proportion: Chap. 23
 CIs for one mean: Chap. 24
 CIs for a mean difference (paired sample mean): Chap. 26
 CIs for the difference between two means: Chap. 27
 CIs for comparing two odds: Chap. 28
 CIs for regression parameters: Sect. 38.6
 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.
 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 or condition of interest, 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.
 Correlational research question
 Correlational RQs explore the relationship between two variables.
 Data
 Data refers items of information obtained from a study (such as height of seedlings, or the type of medication given).
 Dataset
 A dataset refers to a collection of data.
 Descriptive research question
 Descriptive RQs have only a population and an outcome.
 Descriptive study
 Descriptive studies answer descriptive research questions.
 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 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 closely context approximate the real situation of interest.
 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 \(68\)\(95\)\(99.7\) rule.
 Experimental studies (or Experiments)
 Experimental studies (or experiments) study relationships with an intervention.
 Experimenter effect
 See Observer effect
 Explanatory variable
 An explanatory variable may (partially) explain or be associated with changes in another variable of interest (the response variable). In an experimental study, it is the variable that can be manipulated by the researchers.
 External validity
 External validity refers to the ability to generalise the results of the study to the rest of the population, beyond just those in the studied sample. 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 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 may lead to nonsense predictions.
 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. 7.3).
 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:
 Hypothesis tests about a single mean: Chap. 31
 Hypothesis tests about a mean difference (means of paired samples): Chap. 33
 Hypothesis tests comparing two means: Chap. 34
 Hypothesis tests comparing odds (or percentages): Chap. 35
 Hypothesis tests about a correlation: Sect. 37.2
 Hypothesis tests about regression parameters: Sect. 38.7
 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 validity refers to the extent to which a causeandeffect relationship can be established in a study.
A study with high internal validity shows 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 present when researchers can manipulate (or impose) the values of the explanatory variable on the individuals to determine the impact on the response variable.
 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 \(Q_3\)).
 Jittering
 Jittering is when a small amount of randomness is added in either the horizontal or vertical direction (or sometimes both) to separate points that would otherwise be overplotted.,
 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 recorded in the study data.
 Mean
 The mean is one way to measure the 'average' value of quantitative data. The arithmetic mean is the 'balance point' of the data, and 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.
 Mode
 A mode is one way to measure the 'average' value of some qualitative data. A mode is the level (or levels) of a qualitative variable with the most observations.
 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. D.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
 Observational studies study relationships without an intervention.
 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 then 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 a different 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 summarised for a group.
 Outliers
 An outlier is an observation that is 'unusual' (either larger or smaller) compared to the bulk of the data. Rules for identifying outliers are arbitrary.
 \(P\)value
 A \(P\)value is the probability 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, usually unknown, describing some feature of a population, and estimated by a statistic.
 Paired data
 Paired data occurs when the outcome is compared for two different, distinct situations for each individual.
 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.
 Pilot study
 A pilot study is a small test run of the study protocol used to check that the protocol is appropriate and practical, and to identify (and hence fix) possible problems with the research design or protocol.
 Placebo
 A placebo is a treatment with no intended effect or active ingredient, but appears to be the real treatment.
 Placebo effect
 The placebo effect occurs when individuals report perceived or actual effects despite not receiving an active treatment or condition, in experimental studies (Sect. 7.5).
 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.  Population
 A population is a 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 similar the sample estimates from different samples are likely to be to each other (that is, how much variation is likely in the sample estimates).
 Probability
 A probability is a number between \(0\) and \(1\) inclusive (or between \(0\)% and \(100\)% inclusive) that quantifies the likelihood that a certain event will occur. A probability of zero (or \(0\)%) means the event is 'impossible' (will never occur), and a probability of one (or \(100\)%) means that the event is certain to happen (will always occur). Most events have a probability between the extremes of \(0\)% and \(100\)%.
 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 have numerical meaning, and represent quantities or amounts. Quantitative data generally arise from counting or measuring.
 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., allocate the values of the explanatory variable to the individuals), but (2) do not determine who or what is in those groups.
 Questionnaire
 A questionnaire is a set of questions for respondents to answer.
 Random
 Random means 'determined completely by impersonal chance'.
 Random procedure
 A random procedure is a sequence of welldefined steps that (a) can be repeated, in theory, indefinitely under essentially identical conditions; (b) has welldefined results; and (c) has result that are unpredictable from any individual repetition.
 Range
 The range is the maximum value of a variable minus the minimum value of the variable.
 Relational research question
 Relational RQs have a population, outcome, and a betweenindividuals comparison.
 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. This produces an approximate probability.
 Repeatedmeasures research question
 Repeatedmeasures RQs have a population, outcome and a withinindividuals comparison.
 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.
 Research design
 Research design refers to the decisions made by the researchers to maximise external validity and internal validity.
 Response variable
 A response variable records the result, output, consequence or effect of interest from changes in another variable (the explanatory variable).
 Sample
 A sample is a subset of individuals from the population from which data are collected.
 Sample space
 The sample space is a list of all possible and distinct results after administering a random procedure once.
 Sampling distribution
 A sampling distribution is the distribution of a statistic, showing how its value varies in all possible samples.
 Sampling frame
 The sampling frame is a list of all the individuals in the population.
 Sampling mean
 The sampling mean is the mean of the sampling distribution of a statistic.
 Sampling variation
 Sampling variation refers to how the sample estimates (statistics) vary from sample to sample, because every possible sample is different.
 Selection bias
 Selection bias is the tendency of a sample to over or underestimate a population quantity.
 Scale data
 See quantitative data.
 Simple event
 A simple event is a single element of the sample space.
 Simple random sample
 A sample where every possible sample of a given size has same chance of being selected.
 Stacking
 Stacking is when points are plotted above other points with similar values, to separate points that would otherwise be overplotted.,
 Standard deviation
 The standard deviation is, approximately, the average distance of observations 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 the unknown value of the 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, such as the sampling distribution having an approximate normal distribution.. 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.
 \(t\)score
 A \(t\)score measures how many standard deviations a value is from the mean. A \(t\)score is like a \(z\)score. Also see: \(z\)score.
 Treatments
 The treatments are the values of the explanatory variable that the researchers impose upon the individuals in the experimental study.
 True experiment
 In a true experiment, the researchers (1) allocate treatments to groups of individuals (i.e., values of the explanatory variable to the individuals), and and (2) determine who or what is in those groups. While the steps may not happen explicit, they can happen conceptually.
 Unit of observation
 The unit of observation is the 'who' or 'what' that 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 conclusions are made; the smallest independent elements of the population for which information is analysed. 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
 See levels.
 Variable
 A variable is a single aspect or characteristic associated with the individuals, whose values can vary from individual to individual.
 Voluntary response (selfselecting) sample
 A sample where individuals participate if they wish to.
 Withinindividuals comparison
 See Comparison (within individuals), Comparison.
 \(z\)score
 A \(z\)score measures 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. Also see: \(t\)score.