Safmeds

Question 1

Central tendency

Answer 1

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

Question 2

Confounding variable

Answer 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.

Question 3

Content validity

Answer 3

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

Question 4

Correlation coefficient

Answer 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.

Question 5

Raw data

Answer 5

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

Question 6

Repeated measures/within subjects t-test

Answer 6

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

Question 7

Independent samples/between subjects t-test

Answer 7

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

Question 8

Ecological validity

Answer 8

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

Question 9

Experimental hypothesis

Answer 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).

Question 10

Null hypothesis

Answer 10

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

Question 11

Fit

Answer 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).

Question 12

Frequency distribution

Answer 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.

Question 13

Homogeneity of variance

Answer 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.

Question 14

Hypothesis

Answer 14

A prediction about the state of the world.

Question 15

Independent design

Answer 15

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

Question 16

Repeated design

Answer 16

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

Question 17

Interval data

Answer 17

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

Question 18

Ratio data

Answer 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.

Question 19

Nominal data

Answer 19

Data where numbers represent categories or names.

Question 20

Ordinal data

Answer 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.

Question 21

Kurtosis

Answer 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.

Question 22

Likelihood

Answer 22

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

Question 23

Mann-Whitney test

Answer 23

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

Question 24

Mean

Answer 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).

Question 25

Median

Answer 25

The middle score of a test of ordered observations.

Question 26

Mode

Answer 26

The most frequently occurring score in a set of data.

Question 27

Non-parametric tests

Answer 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.

Question 28

Parametric tests

Answer 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.

Question 29

Population

Answer 29

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

Question 30

Qualitative methods

Answer 30

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

Question 31

Quantitative methods

Answer 31

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

Question 32

Quartiles

Answer 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.

Question 33

Sample

Answer 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).

Question 34

Skew

Answer 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.

Question 35

Standard deviation

Answer 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.

Question 36

Test statistic

Answer 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.

Question 37

Validity

Answer 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.

Question 38

Variance

Answer 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.

Question 39

Wilcoxon’s signed-rank test

Answer 39

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

Question 40

Z-score

Answer 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.