Week 1: The Descriptive Stats of Outcomes - How is the Data Distributed and How can we Assess the Distribution COPY Flashcards

1
Q

*

What distribution is needed for parametric tests?

A

A normal distribution

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2
Q

The normal distribution curve is also referred as the

A

bell curve

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3
Q

Normal distribution is symmetrical meaning

A

This means that the distribution curve can be divided in the middle to produce two equal halves

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4
Q

The bell curve can be described using two parameters called (2)

A
  1. Mean (central tendency)
  2. Standard deviation (dispersion)
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5
Q

μ is

A

mean

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6
Q

σ is

A

standard deviation

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7
Q

Diagram shows:

A

e.g., If we move 1σ to the right then it contains 34.1% of the valeues

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8
Q

Many statistical tests (parametric) cannot be used if the data are not

A

normally distributed

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9
Q

The mean is the sum of

A

scores divided by the number of scores

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10
Q

Mean is a good measure of

A

central tendency for roughly symmetric distributions

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11
Q

The mean can be a misleading measure of central tendency in skewed distributions as

A

it can be greatly influenced by scores in tail e.g., extreme values

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12
Q

Aside from the mean, what are the 2 other measured of central tendency? - (2)

A
  1. Median
  2. Mode
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13
Q

The median is where (2)

A

the middle score when scores are ordered.

the middle of a distribution: half the scores are above the median and half are below the median.

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14
Q

The median is relatively unaffected by … and can be used with… (2)

A
  • extreme scores or skewed distribution
  • can be used with ordinal, interval and ratio data.
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15
Q

The mode is the most

A

frequently occurring score in a distribution, a score that actually occurred

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16
Q

The mode is the only measure of central tendency that can be used with

A

with nominal data

17
Q

The mode is greatly subject to

A

sample fluctuations and is therefore not recommended to be used as the only measure of central tendency

18
Q

Many distributions have more than one

A

mode

19
Q

The mean, median and mode are identical in

A

symmetric distribtions

20
Q

For positive skewed distribution, the

A

mean is greater than the median, which is greater than the mode

21
Q

For negative skewed distribution

A

usually the mode is greater than the median, which is greater than the mean

22
Q

Kurtosis in greek means

A

bulge or bend in greek

23
Q

What is central tendency?

A

the tendency for the values of a random variable to cluster round its mean, mode, or median.

24
Q

Diagram of normal kurotsis, positive excess kurotsis (leptokurtic) and negative excess kurotsis (platykurtic)

A
25
Q

What does lepto mean?

A

prefix meaning thin

26
Q

What is platy

A

a prefix meaning flat or wide (think Plateau)

27
Q

Tests of normality (2)

A

Kolmogorov-Smirnov test
Shapiro-Wilks test

28
Q

Tests of normality is dependent on

A

sample size

29
Q

If you got a massive sample size then you will find these normality tests often come out as …. even when your data visually can look - (2)

A

significant
normally disttibuted

30
Q

If you got a small sample size, then the normality tests may look non-siginificant, even when data is normally distributed, due to

A

lack of power in the test to detect a significant effect

31
Q

There is no hard or fast rule for

A

determining whether data is normally distributed or not

32
Q

Plot your data because this helps inform on what decisions you want to make with respect to

A

normality

33
Q

Even if normality test is sig and data looks visually normally distributed then still do

A

parametric tests

34
Q

A frequency distribution or a histogram is a plot of how many times

A

each score occurs

35
Q

2 main ways a distribution can deviate from the normal - (2)

A
  1. Lack of symmetry (called skew)
  2. Pointyness (called kurotsis)
36
Q

In a normal distribution the values of skew and kurtosis are 0 meaning…

A

tails of the distribution are as they should be