Semester 1 Recap

Question 1

What is replicability and reproducibility?

Answer 1

Replicability - The ability of a scientific experiment or trial to be repeated to obtain a consistent result. Research is replicable when the researcher collects new data to arrive at the same scientific findings as a previous study.
Reproducibility - When the original researcher’s data and computer codes are used and regenerate the same results.

Question 2

What is a theory?

Answer 2

An explanation of a particular behaviour or phenomenon, typically based on scientific research

Question 3

What is a hypothesis?

Answer 3

A specific prediction about a behaviour or phenomenon that can be tested in a scientific research project

Question 4

Independent variable (IV)

Answer 4

The variable that is manipulated by the experimenter (under the experimenter’s
control). May be naturally occurring

Question 5

Dependent variable (DV)

Answer 5

The variable that is measured. An outcome variable that changes as a result of the IV.
E.g. reaction times, IQ scores, personality scores, body fat percentage, etc.

Question 6

Control variables

Answer 6

The variables that are kept controlled and constant throughout the study so that they don’t interfere with the dependent variable

Question 7

Extraneous/Confounding variables

Answer 7

Other variables that might have an effect on the relationship between the IV and DV. We try to control these variables.

Question 8

Nominal data

Answer 8

Categorise data by labelling them in groups, no order between the categories. No numerical properties. E.g., city of birth, gender, ethnicity, car brands, marital status.

Question 9

Ordinal data

Answer 9

Categorise and rank data in an order, cannot say anything about the intervals between the rankings. E.g., top 5 Olympic medalists, language ability, likert-type questions

Question 10

Interval data

Answer 10

Ordered scale, intervals between units of measurement are all equal. No absolute zero. E.g., test scores, personality inventories, temperature in Fahrenheit or Celsius

Question 11

Ratio data

Answer 11

Scale with equal intervals and an absolute zero. Negative values not possible. E.g., height, age, weight, temperature in Kelvin

Question 12

Experimental design - Independent groups and repeated measures

Answer 12

Researcher manipulates independent variable(s) and measures the outcome (dependent variable). E.g., T-test, Analysis of variance (ANOVA).
Independent groups (between subjects) - pps randomly assigned to different conditions/groups
Repeated measures (within subjects) - each pp takes part in all conditions

Question 13

Quasi-experimental design

Answer 13

Independent groups design in which pps are not randomly allocated to different conditions of the IV (non-random criteria). IVs that we cannot directly manipulate - gender, smoker, religion, genetics, etc.

Question 14

Correlational design
Limitations of a correlational design

Answer 14

Measures the association (relationship) between variables. No independent variables. E.g., correlation, regression.
Limitations - Cannot infer causation from correlations, extraneous various may cause changes in both measured variables.

Question 15

Categorical design

Answer 15

Measures nominal variables (frequency). E.g., Chi-Square

Question 16

Population and samples
Summary values of population and samples

Answer 16

Population - A group that have something in common that is of interest for the research question.
Sample - Smaller group of members of a population selected to represent that group
Summary values:
- Parameters describe populations (e.g., the mean of a population is a parameter)
- Statistics describe samples (e.g., the mean value of a sample is a statistic)

Question 17

Sampling error

Answer 17

When an analyst does not select a sample that represents the entire population of data, so the results found in the sample do not represent the results that would be obtained from the entire population.
Can be reduced by increasing same size and using random sampling

Question 18

Sampling distribution

Answer 18

The distribution of sample statistics (e.g., means, proportions) obtained from multiple random samples of the same size from a population.
Provides information about the variability and characteristics of the sample statistics.
The shape of the sampling distribution depends on the population distribution and the sample size

Question 19

Central limit theorem

Answer 19

States that, regardless of the shape of the population distribution, the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
A fundamental concept in statistics and allows for the use of normal distribution-based inference methods even when the population distribution is not normal.
Applicable when the sample size is sufficiently large (typically n ≥ 30) or when the population distribution is approximately normal.

Question 20

Standard errors

Answer 20

Measures of the variability or uncertainty associated with a sample statistic.
Quantify the average amount of sampling error expected in estimating a population
parameter.
Directly related to the standard deviation.
SE reflects the precision of the sample statistic estimate, while the standard deviation describes the variability of the individual data point

Question 21

Measures of central tendency

Answer 21

Value that represents a typical (or central) score in a dataset.
Mean, Median, Mode

Question 22

Mean

Answer 22

Arithmetical value. Sum of scores divided by number of scores.
Used on interval or ratio data
Advantages - uses all values, is sensitive
Disadvantages - influenced by outliers

Question 23

Median

Answer 23

Middle score when data points are arranged from smallest to largest.
Used on interval, ratio, and ordinal data
Advantages - not affe ted by outliers
Disadvantages - does not use all data, not very sensitive

Question 24

Mode

Answer 24

Most frequently occurring score
Used on nominal data
Advantages - only measure that can be used for categorical data
Disadvantages - possible to have more than one mode or no mode - not vey useful

Question 25

Measures of dispersion

Answer 25

Value that describes how spread out a set of data is.
Range, Interquartile range, Variance, Standard deviation

Question 26

Range

Answer 26

Highest score minus lowest score
Disadvantage - only based on two scores, ignored other information

Question 27

Interquartile range (IQR)

Answer 27

Put all the scores in order, from smallest to largest:
3, 4, 4, 5, 5, 6, 7, 7, 10, 12, 13, 15, 16, 16, 18
Divide set of scores into four equal parts (quarters):
3, 4, 4, 5 (lower quartile), 5, 6, 7, 7 (median), 10, 12, 13, 15 (upper quartile), 16, 16, 18
The middle two quarters (50%) represent the IQR:
IQR = 15 – 5 = 10
Disadvantage - not based on all observations, first 25% and last 25% are ignored

Question 28

Variance

Answer 28

How much the values in the dataset deviate from the mean
Find the differences between each number in the data set and the mean, square the differences to make them positive, divide the sum of the squares by the number of values in the data set.
Variance = Sum of the squared deviations ÷ (N - 1). E.g.:
0.14 + 2.64 + 5.64 + 0.14 + 2.64 + 5.64 + 0.39 + 2.64 = 19.87
19.87 ÷ (8-1) = 2.84
Disadvantage - doesn’t describe the amount of variability in the same units as the original data (due to squaring the values)

Question 29

Standard deviation (SD)

Answer 29

Square root of the variance. Most popular measure of dispersion.
Large SD - scores are spread out
Small SD - scores are close to the mean

Question 30

Graphical representations

Answer 30

Tools used to present data in a visual format, making it easier to understand and interpret complex information. E.g., box plots and histograms

Question 31

Box plot

Answer 31

Summarise the distribution of a dataset using key summary statistics, such as the minimum, maximum, quartiles, and outliers.
Helpful for comparing distributions and identifying outliers.

Question 32

Histogram

Answer 32

Depict the distribution of numerical data by dividing it into intervals or bins and showing the frequency or count of data points falling into each bin.
Make it easier to identify whether data is unimodal, bimodal, or skewed

Question 33

Outlier

Answer 33

A data point/ observation that differs significantly from other observations; an observation that lies an abnormal distance from other values in a random sample from a population

Question 34

Normal distribution

Answer 34

Also known as Bell Shaped Curve
- Mean = mode = median
- The mean divides the data in half
- Symmetric
- Unimodal curve (i.e., one peak)
- The curve approaches, but never touches,
the x-axis

Question 35

Skewed distribution

Answer 35

Neither symmetric nor normal because the data values trail off more sharply on one side than on the other