SAFMEDS

Question 1

A generic term describing the centre of a frequency distribution of observations, measured by mean, mode, and median.

Answer 1

Central tendency

Question 2

A variable (that may or may not have been measured), other than the independent variable/s, which influences the outcome of the dependent variable

Answer 2

Confounding variable

Question 3

Evidence that the content of a test corresponds to the content of the construct it was designed to cover.

Answer 3

Content validity

Question 4

A measure of the strength and direction of the association between two variables. There are two common variants- Pearson’s for parametric data, and Spearman’s for non-parametric data. In both cases, coefficients range between -1 and 1.

Answer 4

Correlation coefficient

Question 5

A set of data which is yet to be screened for analysis.

Answer 5

Raw data

Question 6

A test using the t-statistic that establishes whether two means collected from the same sample differ significantly.

Answer 6

Repeated measures/within subjects t-test

Question 7

A test using the t-statistic that establishes whether two means collected from independent samples differ significantly.

Answer 7

Independent samples/between subjects t-test

Question 8

Evidence that the results of a study, experiment, or test can be applied, and allow inferences, to real-world conditions.

Answer 8

Ecological validity

Question 9

The prediction that there will be an effect (ie, that an experimental manipulation will have some effect on the dependent variables, or that certain variables will relate to each other).

Answer 9

Experimental hypothesis

Question 10

The reverse of the experimental hypothesis- that there will be no effect from your experimental manipulation, or that certain variables are not related.

Answer 10

Null hypothesis

Question 11

The degree to which a statistical model is an accurate representation of the observed data. These range from basic models (eg, the mean) to more complex models (eg, t-test and correlations).

Answer 11

Fit

Question 12

A graph plotting values of observations on the Y axis, and the frequency with which those values occur on the X axis, commonly called a histogram. Used to assess the distribution of data.

Answer 12

Frequency distribution

Question 13

An assumption for parametric testing in between-groups designs, where the variance of one variable is stable (roughly equal) at all levels of another variable.

Answer 13

Homogeneity of variance

Question 14

A prediction about the state of the world.

Answer 14

Hypothesis

Question 15

An experimental design in which different treatment conditions use different participants, resulting in independent data.

Answer 15

Independent design

Question 16

An experimental design in which different treatment conditions use the same participants, resulting in related or repeated data.

Answer 16

Repeated design

Question 17

Data measures on a scale along which all intervals are equal, for example pain ratings on a scale of 1 to 10.

Answer 17

Interval data

Question 18

Interval data, with the additional property that ratios are meaningful. For example, when assessing pain on a scale of 1 to 10, for the data to be considered ratio level, a score of 4 should genuinely represent twice as much pain as a score of 2.

Answer 18

Ratio data

Question 19

Data where numbers represent categories or names.

Answer 19

Nominal data

Question 20

Data that tell us not only that something occurred, but the order in which it occurred. Examples include data presented as ranks, for example placements on participants in a race.

Answer 20

Ordinal data

Question 21

Measures the degree to which scores cluster at the tails of a frequency distribution; positive kurtosis indicates too many scores in the tails, resulting in a peaked curve. Negative kurtosis indicates too few scores in the tails, resulting in a flattened curve.

Answer 21

Kurtosis

Question 22

The probability of obtaining a set of observations given the parameters of a model fitted to those observations.

Answer 22

Likelihood

Question 23

A non-parametric test which examines differences between two independent samples. The non-parametric equivalent of an independent t-test

Answer 23

Mann-Whitney test

Question 24

A simple statistical model on the centre of a distribution of scores; a hypothetical estimate of a ‘typical’ score, calculated by summing the observed scores and dividing by the number of observations (n).

Answer 24

Mean

Question 25

The middle score of a test of ordered observations.

Answer 25

Median

Question 26

The most frequently occurring score in a set of data.

Answer 26

Mode

Question 27

A family of statistical tests that do not rely on the restrictive assumptions of parametric tests. In particular they do not assume sampling distribution is normal. Normally considered less powerful.

Answer 27

Non-parametric tests

Question 28

A family of statistical tests that require data to meet certain assumptions, in particular around the distribution of the data and the inter-relation between variables levels. The basic assumptions for parametric test are; data are normally distributed, homogeneity of variance, interval or ratio data, and independence of scores.

Answer 28

Parametric tests

Question 29

In statistical terms, this refers to the group from which we draw a sample, and to which we want to generalise results.

Answer 29

Population

Question 30

Extrapolating evidence for a theory from what people say and write (in contrast to quantitative methods).

Answer 30

Qualitative methods

Question 31

Inferring evidence for a theory through measurement of variables that produce numeric outcomes (in contrast to qualitative methods).

Answer 31

Quantitative methods

Question 32

A generic term for the three values that cut an ordered data set into four equal parts. The three sections are known as the lower, middle, and upper quartiles. The space between the lower and upper quartiles is known as the inter-quartile range.

Answer 32

Quartiles

Question 33

A smaller (but hopefully representative) collection of units from a population, used to determine truths about that population (eg, how the population behaves under certain conditions).

Answer 33

Sample

Question 34

A measure of the symmetry of a distribution, with a skew of 0 representing perfect symmetry. When scores are clustered at the lower end of the distribution, the skew is positive. When scores are clustered at the higher end of the higher end of the distribution, the skew is negative.

Answer 34

Skew

Question 35

An estimate of the average variability (spread) of a set of data, measures in the same unit of measurement as the original data. It is the square-root of the variance.

Answer 35

Standard deviation

Question 36

A statistic for which we know how frequently different values occur in random samples. The observed value of such a statistic is usually used to test a hypothesis.

Answer 36

Test statistic

Question 37

Evidence that a study allows correct inferences about the question it was designed to answer, or that a test measures what is set out to measure.

Answer 37

Validity

Question 38

An estimate of the average variability (spread) of a set of data. It is the sum of the squares divided by the number of values on which the sum of the squares is based, minus 1.

Answer 38

Variance

Question 39

A non-parametric test that looks for differences between related samples. A non-parametric equivalent of the related t-test.

Answer 39

Wilcoxon’s signed-rank test

Question 40

The value of an observation expressed in standard deviation units, calculated by taking the observed score and subtracting the sample mean, then dividing the result by the standard deviation of all observations. Z-scores can be used to assess the likelihood of obtaining certain scores on a measure.

Answer 40

Z-score