Stats - data types, descriptive, scales of measurement and inferential statistics

Question 1

What type of data?

Observed values can be put into set categories which have no particular order, direction or hierarchy. You can count but not order or measure nominal data

Answer 1

Nominal

Examples:
- genotype
- blood type
- zip / post code
- biological sex
- race
- eye color
- political party

Question 2

What type of data?

Observed values can be put into set categories which themselves can be ordered (for example NYHA classification of heart failure symptoms) - rankings, orders or scales.

Note: no certainty that the intervals between the values are equal

Answer 2

Ordinal

Examples:
- socio economic status (‘low income’,’middle income’,’high income’)
- education level (‘high school’,’BS’,’MS’,’PhD’)
- income level (‘less than 50K’, ‘50K-100K’, ‘over 100K’)
- satisfaction rating (‘extremely dislike’, ‘dislike’, ‘neutral’, ‘like’, ‘extremely like’)

Question 3

What type of data?

Observed values are confined to a certain values, usually a finite number of whole numbers (for example the number of asthma exacerbations in a year)

Answer 3

Discrete

Question 4

What type of data?

Data can take any value with certain range (for example weight)

Answer 4

Continuous

Question 5

What type of data?

Data may take one of two values (for example gender)

Answer 5

Binomial

Question 6

What are the 4 hierarchical levels of measurement when it comes to data?

Answer 6

1) nominal (categories)
2) ordinal (rank order)
3) interval (equal spacing)
4) ratio (true zero)

Each one down the list has all the qualities as the one above it plus extra (in brackets)

Question 7

What type of data?

A measurement where the difference between two values is meaningful, such that equal differences between values correspond to real differences between the quantities that the scale measures - no true zero

Answer 7

Interval

• temperature (Farenheit)
• temperature (Celcius)
• pH
• credit score

Question 8

What type of data?

A measurement where not only intervals but also ratios between numbers are meaningful due to a non-arbitrary zero point (for example weight, height) i.e a true zero exists

Answer 8

Ratio

examples:
- enzyme activity
- dose amount
- reaction rate
- concentration
- pulse
- weight
- length
- temperature in Kelvin (0.0 Kelvin really does mean ‘no heat’)
- survival time

Question 9

Ratio and interval data represent what larger data type?

Answer 9

Quantitative

Quantitative variables take on numeric values and can be further classified into discrete and continuous types. A discrete variable is one whose values vary by specific finite steps (e.g. Number of siblings). A continuous variable on the other hand, can take any value. Quantitative variables can also be subdivided into interval and ratio types.

Question 10

Ordinal and nominal data represent what larger data type?

Answer 10

Qualitative

Qualitative variables do not take on numerical values and are usually names. Some qualitative variables have an inherent order in their categories (e.g. Social class) and are described as ordinal. Qualitative variables are also called categorical or nominal variables (the values they take are categories or names). When a qualitative variable has only two categories it is called a binary (dichotomous or attribute) variable.

Question 11

Examples of which type of STATISTICS include: measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation), and graphical representations (histograms, bar charts, scatter plots).

Answer 11

Descriptive

Question 12

Examples of which type of STATISTICS include: hypothesis testing, confidence intervals, regression analysis, and analysis of variance (ANOVA)

Answer 12

Inferential

Question 13

Which form of STATISTICS summarises and organises data so that it can be understood more easily. These statistics describe the basic features of a dataset, providing simple summaries about the sample and the measures.

Answer 13

Descriptive

Question 14

Which form of STATISTICS allow us to make generalisations and draw conclusions about a population based on a sample of data. These statistics are used to infer trends and make predictions.

Answer 14

Inferential

Question 15

In a clinical setting, which statistics might be used to summarise the demographic characteristics of a patient group, such as the average age, gender distribution, or common diagnoses within a sample of patients, helping in understanding the basic profile of the sample?

Answer 15

Descriptive

Question 16

Which type of statistics come into play when researchers or clinicians want to determine whether the findings from a sample of patients can be generalised to a broader population?

For instance, a psychiatrist might use this type of statistics to test whether a new treatment for depression is more effective than the standard treatment based on data from a clinical trial. This involves making conclusions about the treatment’s effectiveness for the entire patient population, not just the sample studied.

Answer 16

Inferential

Question 17

What is the difference between descriptive and inferential statistics in terms of:

1) purpose

Answer 17

Purpose: Descriptive statistics aim to describe the sample, while inferential statistics aim to make inferences about the population from which the sample is drawn.

Question 18

What is the difference between descriptive and inferential statistics in terms of:

2) techniques

Answer 18

Techniques: Descriptive statistics use measures such as mean, median, mode, and standard deviation. Inferential statistics use techniques like t-tests, chi-square tests, regression analysis, and ANOVA to draw conclusions.

Question 19

What is the difference between descriptive and inferential statistics in terms of:

3) data scope

Answer 19

Data Scope: Descriptive statistics deal with the data at hand. Inferential statistics involve making predictions or generalisations beyond the immediate data.

Question 20

What is the difference between descriptive and inferential statistics in terms of:

4) clinical relevance

Answer 20

Descriptive Statistics: A psychiatrist might use descriptive statistics to report on the average improvement scores of patients after a treatment programme, providing a clear summary of the observed data.

Inferential Statistics: When determining the efficacy of a treatment across different populations, a psychiatrist would use inferential statistics to assess whether observed differences in outcomes are statistically significant and not due to chance. This helps in making evidence-based decisions about treatment protocols.