2 (1) Statistics

Question 1

What do we use statistics for?

Answer 1

Describing data, applying normative data to clinical practice, looking for associations, seeing whether variables are similar or different and if this is down to chance.

Question 2

Pros and cons of using statistics

Answer 2

Concise - can filter down info into numbers
Generalisation of findings to wider population

Numbers remove context and meaning
Still need qualitative data, as numbers can’t convey subtle differences

Question 3

Types of variables

Answer 3

Got category/scale
And within scale (ordinal versus interval/ratio)

• discrete or nominal or categorical
• ordinal variables
• Continuous or scale variables

Question 4

Discrete/nominal/categorical

Answer 4

Classify data into categories (e.g. gender)

Example: Y or N.

Question 5

Ordinal variables

Answer 5

Order matters but not the actual differences between the numbers

Examples self rating scales

Question 6

continuous/scale variables

Answer 6

Values are along the scale. There is order; differences in magnitude.

Example: age, income, grades

Question 7

How would you describe scale variables?

Answer 7

Data distribution: Normal and skewed distribution.

Question 8

In what case could we put the distribution of the data in histogram

Answer 8

If the date is continuous and we have enough data points.

Question 9

What is normal distribution?

Answer 9

After plotting frequencies on a histogram, we can get a symmetrical bell-like curve. This is known as normal distribution. The largest portion cluster in the middle.

The relevant values are mean and standard to you soon.

Question 10

What’s skewed distribution?

Answer 10

Distribution (on histogram) is not symmetrical.

The relevant values are the median and range.
Skewness value must be above +1 and below -1.

Question 11

Positive versus negative skew.

Answer 11

Positive skew: most people score in the lower range. Mean>median.

Negative skew: most people score in the high range.
Median>mean.

Question 12

Bimodal distribution

Answer 12

Two or more central clusters

Question 13

What are the measures of central tendency?

Answer 13

• mean
• median
• mode

Question 14

Mean

Answer 14

Average score obtained by adding all the scores and dividing the number of cases

Question 15

Median

Answer 15

Results are put in numerical order and the middle value is found. It is less affected by extreme scores.

Question 16

Mode

Answer 16

Most freq occurring number

Question 17

How can mean median and mode be affected?

Answer 17

More normal the distribution, the closer the three measure of central tendency are.

Mean is sensitive to the range of data and outliers.

In normal distribution: mean is good descriptor of data.
Skewed distribution: median better descriptor

Question 18

Range and outlier

Answer 18

Range: highest and lowest score; minimum and max

Outlier: value in data that is markedly different to the others

Question 19

Aside from using mean, median, mode, what’s another way of looking at the spread of data?

Answer 19

Calculate the difference between each score and mean.

Variance: the average of the squared differences from the mean

Standard deviation: measure of how spread out the date is = the square root of variance.

Question 20

What’s standard deviation and how is it reported?

Answer 20

It’s a measure of how spread out the data is.

Always reported with the mean so MEAN(SD)
When SDs overlap, we are less confident in the results.

Question 21

SDs and percentages

Answer 21

NORMAL, 0-1 SD➡️ 68% (34% above and below the mean)
+/-1 to 2 SD ➡️ 14%
2-3 SD ➡️ 2%

Question 22

What’s interquartile range?

Answer 22

It reports only 50% of the data range. Located in the middle of distribution.

4 quartiles. Median is in the middle of the IQR.

Question 23

What’s standard error?

Answer 23

It’s a measure of how accurate an estimate of the population mean our sample mean is.

• 95% confidence ➡️ 95% pop. fall in range (5% standard error)

The smaller the SE, the better the sample mean is an estimation of the population mean.

Question 24

How would we describe/report categorical data?

Answer 24

Reporting the frequency of cases of each category; reporting the percentage frequency of cases in each category.

Plot discrete data on pie or bar charts.