W2: Practical Flashcards

1
Q

What does this show?

A

drug 1 was scoring better than the placebo group and drug 2 was scoring additional benefit of this new drug

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2
Q

For interaction effect a

A

clustered bar chart is better

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3
Q

What is an interaction effect?

A

An interaction effect occurs when the effect of one variable depends on the value of another variable.

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4
Q
  • Line graphs can be quite useful when you got
A

time series data (measurements over many time points)

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5
Q

Example of interaction effect - (3)

A
  • So for drug 1, this seemed to be as effective as drug 2 for early onset Alzheimer
  • Drug 1 not very effective for late onset Alzhiemer and not much difference between drug 1 and placebo for that group
  • Whereas, drug 2 seems to be more effective for both types of early and late onset Alzheimer’s
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6
Q

What are z-scores?

A

A measure of variability: The number of standard deviations from the population mean or a particular data point is

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7
Q

Z scores are a standardised measure and ignore

A

measurement units

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8
Q

Why should I care about Z scores? - (2)

A

Z-scores allow researchers to calculate the probability of a score occurring within a standard normal distribution

Enables us to compare two scores that are from different samples (which may have different means and standard deviations)

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9
Q

How to read positive z score table to get percentile? - (2)

A

first colum contains first part of z score (whole number and decimal point)

top row contains remaining deicmal point

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10
Q

How to read positive z score table to get percentile? example- (2)

A

if z score is 1.25 then.. look left column for 1.2 and top row for 0.05

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11
Q

If trish takes a test and gets a score of 25 and shows her z score is 1.25 and percentle is 0.8944 it shows that

A

89.4% of students performed worse than Trish

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12
Q

Who performed better Trish or Josh?
89.4% students performed worse than Trish
84.1% students performed worse than Josh

A

Trish

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13
Q

68% of scores are within

A

1 SD of mean

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14
Q

95% of scores are within

A

2 SDs of mean

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15
Q

99.7% of scores are within

A

3 SDs of the mean

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16
Q

narrow CIs indicate higher

A

power

17
Q

wider CIs indicate

A

low statistical power (bad).

18
Q

If CIs overlap shows

A

two means not significantly different

19
Q

If CIs do not overlap it shows

A

two means are significantly different

20
Q

Null hypothesis is typically a hypothesis of

A

no difference (0)

21
Q

We assume the null hypothesis is

A

true

22
Q

We collect evidence to REJECT the

A

null hypothesis

23
Q

We can never say that the null hypothesis is

A

FALSE

24
Q

TheP valueor calculated probability is the estimated probability of us

A

finding an effect when the null hypothesis (H0) is true.

25
Q

p value equals to

A

probability of observing a test statistic at least as a big as the one we have if the null hypothesis were true.

26
Q

Statistical significance does not equal

A

importance

27
Q

The reason why statistical significance does not equal importance due to 2 reasons - (2)

A
  1. p = 0.049, p = 0.050 are essentially same and former is statistically sig
  2. Importance is dependent upon exp design/aims
28
Q

Statistical Sig does not equal importance as importance dependent upon experimental design/aims - example

A

A statistically significant weight increase of 0.1Kg between two adults experimental groups may be less important than the same increase between two groups of babies.