Summarising Data

Question 1

Why is it important to summarise data in neuroscience?

Answer 1

Using statistics to summarise data allows it to be presented and communicated, as well as quantifying the variation and uncertainty in the data.

Question 2

CLARITY AND UNDERSTANDING

Answer 2

Distil large amounts of complex data into an understandable form. Allows easier identification of patterns and insights.

Question 3

HYPOTHESIS TESTING

Answer 3

Allows focus on the essential data to allow more accurate conclusions to be formed about whether hypotheses are supported or refuted.

Question 4

EFFICIENT COMMUNICATION

Answer 4

Summarised data can be clearly and concisely presented for sharing in journals, conferences etc.

Question 5

RESOURCE MANAGEMENT

Answer 5

Focussing on the most relevant data allows time, resources and funding to be focussed to ensure that research is impactful.

Question 6

META-ANALYSES AND GENERALISATION

Answer 6

Summarised data is needed to combine results from multiple studies to increase statistical power and generalisability of findings.

Question 7

DATA INTEGRITY AND REPRODUCIBILITY

Answer 7

Clearly documented summarised data allows researchers to replicate experiments.

Question 8

DEVELOPMENT OF THEORIES

Answer 8

Summarised data allows identification of consistent patterns, allowing theoretical models to be developed and refined.

Question 9

What are nominal data?

Answer 9

Categorical data without a specific order e.g., blood types.

Question 10

What are ordinal data?

Answer 10

Categorical data with a meaningful order but no consistent interval between categories e.g., stages of cancer.

Question 11

What are discrete data?

Answer 11

Countable values (typically integers) e.g., number of hospital visits.

Question 12

What are continuous data?

Answer 12

Values can fall anywhere within a specified rang e.g., blood pressure.

Question 13

What are interval data?

Answer 13

Numerical data with meaningful intervals between values but without a true zero point e.g., temperature.

Question 14

What are ratio data?

Answer 14

Numerical data with equal intervals and a true zero point which allows for the calculation of ratios e.g., height, weight.

Question 15

MEASURES OF CENTRAL TENDENCY

Answer 15

Mean

Median

Mode.

Question 16

MEASURES OF SPREAD

Answer 16

Range

Variance (average of squared differences from the mean)

Standard deviation (square root of variance)

IQR (range between 25th and 75th percentile)

Question 17

MEASURES OF SHAPE

Answer 17

Skewness (positive - tail on right, negative - tail on left)

Kurtosis (tailedness of data distribution)

Question 18

GRAPHICAL SUMMARIES

Answer 18

Histograms (frequency distributions)

Box plots (median, quartiles, potential outliers)

Scatter plots (relationship between 2 quantitative variables)

Bar charts (categorical data)

Question 19

SUMMARY TABLES

Answer 19

Frequency tables (number of occurrences in each category)

Contingency tables (frequency distribution of variables - shows relationship between them)

Question 20

CORRELATION AND ASSOCIATION

Answer 20

Correlation Coefficient (measures strength and direction of relationship between 2 variables)

Covariance (indicates direction of linear relationship between 2 variables)

Question 21

REGRESSION MODELS

Answer 21

Linear Regression (models relationship between dependent and independent variable(s) by fitting a linear equation to data - predicts value of dependent variable based on value(s) of independent variable(s))

Logistic Regression (models probability of event using binary outcome variables)

Poisson Regression (counts data and rates - for example the number of event occurrences within a fixed period)

Question 22

LONGITUDINAL DATA ANALYSIS

Answer 22

Mixed-Effects Models (accounts for fixed and random effects - useful for measurements taken on the same subjects over time)

Generalised Estimating Equations (estimates parameters of a generalised linear model with a possible unknown correlation between outcomes)

Question 23

SURVIVAL ANALYSIS

Answer 23

Kaplan-Meier Estimator (estimates survival function from lifetime data)

Cox Proportional Hazards Model (assesses effect of variables on survival time and estimates hazard ratios)

Question 24

MULTIVARIATE ANALYSIS

Answer 24

Principle Component Analysis (reduces dimensionality of data whilst retaining most of the variance - identifies patterns and simplifies datasets)

Factor Analysis (identifies underlying relationships between variables by grouping them into factors)

Question 25

BAYESIAN METHODS

Answer 25

Bayesian Inference (updates probability of a hypothesis as more evidence becomes available) Markov Chain Monte Carlo (samples from a probability distribution to perform Bayesian inference)

Question 26

ADVANCED VISUALISATION TECHNIQUES

Answer 26

Heatmaps (show intensity of data points) Network Analysis (visualises relationships between entities)

Question 27

What type of outlier falls outside inner fences?

Answer 27

A minor outlier.

Question 28

What type of outlier falls outside outer fences?

Answer 28

A major outlier.

Question 29

How do you calculate inner fence boundaries?

Answer 29

(Q3-Q1) x 1.5 Add to Q3 Subtract from Q1

Question 30

How do you calculate outer fence boundaries?

Answer 30

(Q3-Q1) x 3 Add to Q3 Subtract from Q1

Question 31

Why would you transform data?

Answer 31

To transform the data into a different scale to allow interpretation and/or statistical analysis.

Question 32

What reasons are there for transforming data?

Answer 32

1. To improve normality (to allow use of parametric tests) 2. To reduce skewness 3. To linearise the relationship between 2 variables 4. To make multiplicative relationships additive

Question 33

What are some commonly used data transformations?

Answer 33

1. Natural logarithm transformations 2. Power transformations

Question 34

When would you perform a log transformation?

Answer 34

If the data are positive values and positively skewed - log transformations stretch the scale at the lower end and compress the scale at the upper end.