Descriptive Stats / Intro

Question 1

A frequency distribution table is a summary table that shows the number of occurrences (frequency) of different values or ranges of values in a dataset.

Answer 1

A frequency distribution table

Question 2

a graphical representation of the distribution of a dataset, displaying the frequencies of data values within specific intervals or bins.

Answer 2

A histogram

Question 3

Table that shows the frequencies or proportions up to a certain point in a dataset, providing a running total of the frequencies.

Answer 3

A cumulative distribution table

Question 4

refers to a distribution with kurtosis equal to the normal distribution, indicating a moderate peakedness and tail behaviour.

Answer 4

Mesokurtic kurtosis (normal Kurtosis)

Question 5

s a distribution with a higher peak and heavier tails than the normal distribution, indicating more extreme values.

Answer 5

Leptokurtic kurtosis (positive kurtosis)

Question 6

distribution with a lower peak and lighter tails than the normal distribution, indicating fewer extreme values.

Answer 6

Platykurtic kurtosis (negative kurtosis)

Question 7

What is the difference between inferential and descriptive statistics?

Answer 7

Descriptive statistics summarise and describe data, while inferential statistics make predictions or inferences about a population based on a sample.

Question 8

What are descriptive statistics?

Answer 8

Descriptive statistics are methods used to summarise and describe the main aspects of a dataset, such as central tendency, variability, and distribution.

Question 9

What are the main aspects of a dataset that descriptive statistics summarise

Answer 9

Central tendency
Variability
distribution

Question 10

If the numbering scheme is arbitrary then it’s probably best to use the —– as a measure of central tendency.

Answer 10

Mode

Question 11

If your data are ordinal scale you’re more likely to want to use the ——- as a measure of central tendency.

Answer 11

median

(The median only makes use of the order information in your data (i.e., which numbers are bigger) but doesn’t depend on the precise numbers involved. That’s exactly the situation that applies when your data are ordinal scale. The mean, on the other hand, makes use of the precise numeric values assigned to the observations, so it’s not really appropriate for ordinal data.)

Question 12

The —— has the advantage that it uses all the information in the data (which is useful when you don’t have a lot of data). But it’s very sensitive to extreme, outlying values.

Answer 12

mean

Question 13

——- of the data. That is, how “spread out” are the data? How “far” away from the mean or median do the observed values tend to be?

Answer 13

variability

Question 14

the 50th percentile is the same as the ——– value

Answer 14

median

Question 15

The —— ——– (—-) is like the range, but instead of the difference between the biggest and smallest value the difference between the 25th percentile and the 75th percentile is taken.

Answer 15

The interquartile range (IQR)

Question 16

Variability
Mean absolute deviation

Answer 16

deviations, added and averaged

Question 17

what is the RMSD

Answer 17

“root mean squared deviation”

Question 18

Properties of distributions.

Answer 18

• What the central tendency is (mean, median or mode).
• How symmetrical the data is either side of the mean (skew).
• How variable the data is (e.g. data range, standard deviation and kurtosis). * If it’s a “normal distribution”

Question 19

It’s often extremely useful to try to condense the data into a few simple “summary” statistics. In most situations, the first thing that you’ll want to calculate is a measure of ——- ———

Answer 19

central tendency

Question 20

If your data are nominal scale you probably why shouldn’t you be using either the mean or the median.

Answer 20

Both the mean and the median rely on the idea that the numbers assigned to values are meaningful. If the numbering scheme is arbitrary then it’s probably best to use the Mode instead.

Question 21

f your data are ordinal scale you’re more likely to want to use the median than the mean because

Answer 21

The median only makes use of the order information in your data but doesn’t depend on the precise numbers involved.

(The mean, makes use of the precise numeric values so it’s not really appropriate for ordinal data.)

Question 22

For interval and ratio scale data you can use :

Answer 22

either one (median or mean) is generally acceptable.

(Which one you pick depends a bit on what you’re trying to achieve. The mean has the advantage that it uses all the information in the data (which is useful when you don’t have a lot of data). But it’s very sensitive to extreme, outlying values.)

Question 23

there are systematic differences between the mean and the median when

Answer 23

the histogram is asymmetric (Skew and kurtosis)

(average income example - median is more appropraiate as mean will give an exaggerated view )

Question 24

The mean can be rememebered as the

Answer 24

centre of garvity or the balancing point of the data

Question 25

out of “mean absolute deviation” (from the mean)
and
“median absolute deviation” (from the median).

which seems to be the better of the two?

Answer 25

the measure based on the median seems to be used in statistics and does seem to be the better of the two.

(But to be honest I don’t think I’ve seen it used much in psychology.)

Question 26

X hat equals

Answer 26

The Mean

Question 27

deviation from the mean

Answer 27

Score - Xhat

(fisrt step in absolute deviation from the mean)

Question 28

Absolute deviation from the mean -

Answer 28

deviation from the mean (avaeraged)

Question 29

The variance of a data set X
is sometimes written as Var( X)
, but it’s more commonly denoted S2

Answer 29

s2 (s-squared)

Question 30

How does jamovi calculate varience differently?

Answer 30

divides by N-1

Question 31

RMSD Root Mean Squared Deviation is

Answer 31

the square root of the varience

Question 32

What are the two catagories of descriptive stats?

Answer 32

Measures of Central Tendencies and measures of Dispersion

Question 33

Take the square root of the variance, known as the standard deviation, also called the

Answer 33

“root mean squared deviation”, or RMSD.

Question 34

range, varience and standard deviation are all measures of

Answer 34

Measures of Dispersion

Question 35

the standard deviation is derived from the ——-

Answer 35

variance

Question 36

In general, you should expect –% of the data to fall within 1 standard deviation of the mean, –% of the data to fall within 2 standard deviation of the mean, and –% of the data to fall within 3 standard deviations of the mean

Answer 36

68, 95, 99.7

(but it’s not exact. It’s actually calculated based on an assumption that the histogram is symmetric and “bell shaped”) it is approximately correct

Question 37

Gives you the full spread of the data. It’s very vulnerable to outliers and as a consequence it isn’t often used unless you have good reasons to care about the extremes in the data.

Answer 37

Range

Question 38

Tells you where the “middle half” of the data sits. It’s pretty robust and complements the median nicely. This is used a lot.

Answer 38

Interquartile range

Question 39

Tells you how far “on average” the observations are from the mean. It’s very interpretable but has a few minor issues (not discussed here) that make it less attractive to statisticians than the standard deviation. Used sometimes, but not often.

Answer 39

Mean absolute deviation

Question 39

Tells you the average squared deviation from the mean. It’s mathematically elegant and is probably the “right” way to describe variation around the mean, but it’s completely uninterpretable because it doesn’t use the same units as the data. Almost never used except as a mathematical tool, but it’s buried “under the hood” of a very large number of statistical tools.

Answer 39

Variance

Question 40

the — and the —— ——– are easily the two most common measures used to report the variability of the data.

Answer 40

IQR and the standard deviation

But there are situations in which the others are used. I’ve described all of them in this book because there’s a fair chance you’ll run into most of these somewhere.

Question 40

This is the square root of the variance. It’s fairly elegant mathematically and it’s expressed in the same units as the data so it can be interpreted pretty well. In situations where the mean is the measure of central tendency, this is the default. This is by far the most popular measure of variation.

Answer 40

Standard deviation

Question 41

A Standard score is referred to as

Answer 41

Z-score

Question 42

The standard score is defined as

Answer 42

the number of standard deviations above the mean that my score lies

Question 43

standard score (z-score) =

Answer 43

35-mean divided by sample

Question 44

—– ——- allow you to interpret a raw score in relation to a larger population (and thereby allowing you to make sense of variables that lie on arbitrary scales),

Answer 44

standard scores (z-scores)

Question 45

stadard scores can also be used to

Answer 45

compared to one another in polls where the raw scores arescaled idfferently to each other.