Week 4

Question 1

What’s the importance of visuals in statistics?

Answer 1

-may have similar mean yet different spread of data which we can’t see solely looking at the numbers
-dots closer to line=shows stronger relationship between variables

Question 2

What are histograms good at?

Answer 2

showing the distribution of data where you can have:
-symmetric data
-skewed right (+ skew like toes on right foot)
-skewed left (- skew like toes on left foot)

Question 3

How does a stem and leaf plot work?

Answer 3

put the tens in a column and the units/end digits in a list
5 8
6 26778
7 14555 (e.g. one is 75)
We could compare the 2 data sets using one stem and leaf plot.
can do mode e.g., 75 median and mean

Question 4

What are boxplots useful for?

Answer 4

-Boxplots are useful for showing medians, ranges, IQ ranges,
skewness etc.
-We could also compare the 2 data sets using box plot

Question 5

How can you tell if boxplots are skewed?

Answer 5

right/+ly skewed = most data on the upper end of the scale
left/-ly skewed= most data trailing on the lower end of the scale

Question 6

What is a normal distribution like?

Answer 6

normally distributed data fits nicely under a bell-shaped curve.
allowing us to do better and more accurate statistical tests.

Question 7

Name the two ways in which a distribution can deviate from
normality

Answer 7

– Lack of symmetry (skewness)
– Pointiness (kurtosis)

Question 8

What is an example of frequency distribution?

Answer 8

■ Histograms
■ They’re individual frequency bars
■ Each bar gives the frequency of a given value e.g., we can count how many people have a healthy heart rate

Question 9

What is an example of probability distribution?

Answer 9

■ Bell curves
■ They’re smooth, but segmented by SDs
■ Area under curve is the the probability that value occurs
■ We can work out the likelihood of a person having a healthy heart rate e.g.

Question 10

True or false: outliers have a bigger impact on smaller sized samples

Answer 10

True

Question 11

Define skewness

Answer 11

■ Skewness is deviation from symmetry.
■histograms show a big difference between means, medians
and mode=skewed data
■ Skewness means some extreme scores are affecting the mean.

Question 12

Define kurtosis (i.e. pointiness)

Answer 12

■ Kurtosis is a measure of the tailedness of a distribution
■ Tailedness = How often outliers occur
■ Three types = Mesokurtic (AKA zero, AKA normal); Leptokurtic
(AKA positive/thin); Platykurtic (AKA negative/flat)

Question 13

What is Kurtosis: Leptokurtic

Answer 13

■ + kurtosis
■ High peak
■ Lepto = skinny (in the middle)
■ Fat tails (big gap underneath? check) (outliers): signifies
either lots of outliers or
occasional outliers which are
very extreme

Question 14

What is Kurtosis: Platykurtic

Answer 14

■ Negative kurtosis
■ Flatter distribution
■ Platy = Broad (in the middle)
■ Skinny tails (outliers):
signifies few outliers or
outliers not so extreme

Question 15

Why is the distribution so important?

Answer 15

■ Tells us which measure of central tendency/dispersion represents our sample best/to use normal distribution=mean and
standard deviation skewed data=median and ranges.
■ Also tells us which inferential statistics we should use.

Question 16

How can you assess the distribution of data using SPSS/histograms?

Answer 16

-Perfectly normally distributed data has a skewness of 0.
-skewness statistic is > twice the standard error=data likely skewed.

Question 17

Define standard error

Answer 17

An approximate standard deviation of the population. I.e. how far is the mean of our sample likely to be from the population mean.

Question 18

What are the rules in producing a table?

Answer 18

– Labelled and titled.
– Placed at the top of the most appropriate page.
– Font and size should be the same as the main text.
– Logical and easy to understand.

Question 19

Define figures

Answer 19

all other visuals that are not tables e.g. bar charts,scatter plots

Question 20

What 2 types of bar charts can you have?

Answer 20

1.simple (bars separate)
2.clustered (bars together)

Question 21

Explain what error bars are

Answer 21

-a visual representation of variability within your data. (on bar charts like the lines)
-Error bars hint at statistical significance
-two confidence intervals do not overlap=difference between
parameters will be significant.
-two confidence intervals do overlap=difference between two parameters can be significant or non-significant.
■ But – we have our p-values to tell us about statistical significance.

Question 22

What’s the most common confidence interval used in error bars?

Answer 22

-95% Confidence intervals are
-It’s the % of times you expect to reproduce an estimate (e.g., a
mean) within the range.
■ E.g., You are confident that 95 out of 100 times the estimate will fall between the upper and lower values specified by the confidence interval.

Question 23

When do the figures go in the appendix/results section?

Answer 23

A=When using figures to assess data (for you) e.g., Stem and leaf plots, boxplots and histograms which are used to assess distribution (e.g., skewness).

R=Figures used to visually present data analysis (for others) e.g., Bar graphs and scatterplots which are not used to assess data.

Question 24

Summarise this lecture

Answer 24

■ Visual aids are useful tools to assess/explain some aspects
of the data.
■ When assessing data, they can show measures of central
tendency/measures of spread.
(put in the appendix of reports.)
■ When explaining data, they can show differences or
relationships in the data.
(in the results section of reports.)