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Quan RM > Lecture 6 > Flashcards

Lecture 6 Flashcards

1
Q

If P is bigger than 0.05, is it significant?

A

No - it needs to be smaller than 0.05. Therefore we can be 95% sure the results didnt occur by chance and can then reject the null.

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2
Q

What does the P value represent?

A

The probability of the null hypothesis being true

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3
Q

What are the 4 types of probability distributions?

A

1) Normal Distribution
2) Log-normal distribution
3) Binomial distribution
4) Poisson distribution

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4
Q

What are probability distributions important for?

A 
Event probabilities (p values) and outliers
Confidence in uncertainty
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5
Q

What are the properties of Log-normal distribution?

A
  • Where logarithm (mathematical function) is normally distributed
  • Asymmetrical with a right skew (postive skew)
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6
Q

What is log-normal distribution common in?

A
  • Biology
  • Natural events
  • Finance
  • Human reaction times?
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7
Q

What transformation yields normal data from log-normal distributions?

A

log-transformation

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8
Q

What data is binomial distribution used for? and define it!

A

Binary data - only with 2 values, recorded as a 0 or a 1

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9
Q

What is the key parameter in binary data?

A

Probability of success (p) - often a %

- from 0-1, if p=0.5, there is an equal amount of 0’s and 1s

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10
Q

What are the properties of Poisson distribution

A
  • Asymmetrical (related to binomial)

- Single parameter: lambda

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11
Q

Define ‘lambda’

A

Expected number of events, based on average rate and interval size

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12
Q

What are the key parameters in poisson distribution?

A

Lambda

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13
Q

What does poisson distribution show?

A

The probability that a given number of events occur independently in a fixed time/ space interval.

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14
Q

What type of data does poisson distribution show?

A

Count data - key parameter = number of events counted

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15
Q

Define count data (poisson distribution)

A

Count of events in a fixed period of time/ space - only whole numbers are possible. For instance - eye blinks per minute. It is asymettrical when count is very low.

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16
Q

What are the criteria for choosing the appropriate probability density function?

A

Type of data (e.g. binary) and match to the shape of data distribution

17
Q

What is the name of the t-test equivalent for binomial data?

A

binomial test

18
Q

What stats tests is used for poisson distribution?

A

Exact rate ratio tests

19
Q

If data is asymetric, what should your confidence intervals be?

A

Asymmetric as well

20
Q

What % is 3 sigma equivalent to?

A

99.7%

21
Q

What confidence interval is most commonly used?

A

95%

22
Q

What is the 95% confidence interval based on?

A

2 SD away from the mean (2 sigma)

23
Q

What are data values outside of confidence intervals called?

A

Outliers

24
Q

What are the methods of dealing with outliers?

A
  • Exclude them
  • Transform data
  • Run analyses with and without outliers to see if they are having an effect on your data
25
Q

What are the purposes of data transformations?

A

To match the shape of data distribution to a known, plausible pdf, giving us new data

26
Q

What are the benefits of data transformations?

A
  • doesnt always change distribution shape (e.g. largest remains so)
  • Makes data better for analysis
27
Q

When would you use log transformation?

A

When data has a right hand skew and could be log normal

28
Q

How does log transformations reduce skew?

A

Reduces larger values more than smaller ones

29
Q

How do you do a log transformation on SPSS?

A

Transform - compute variable - lg10 (logname of variable)

30
Q

Why would use a Z-Score transformation/

A

to normalise the scale of distribution to mean = 0, SD =1. Basically puts all values on a scale from -1 to 1 with a mean of 0

31
Q

Whats a benefit of z-score transformations?

A

Doesnt affect shape of distribution

32
Q

How do you do a z-score transformation on SPSS?

A

Analyse - descriptive statistics- enter variables - select ‘save standardised values’

33
Q

What is the process of a rank transformation?

A

Assings 1 to the lowest value, 2 to the next. Giving the same scores for the same values

34
Q

what is a benefit of a rank transformation?

A

Deals with heavily skewed/ otherwise difficult data

35
Q

How do you do a rank transformation on SPSS?

A

Transform - rank scores - enter variables

36
Q

What is the 1st step of data cleaning?

A

Check for reasonable values, e.g. height shouldnt contain 0 or 390

37
Q

What is the 2nd step of data cleaning?

A

Check for floor/ ceiling effects - where data points bunch up around lowest/ highest possible value

38
Q

What is the 3rd step of data cleaning?

A

Check distribution shape, apply transformations if neccessary, deal with outliers.

Quan RM (9 decks)

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