Flashcards in BIO 330 Deck (379)

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61

## CV used for

### relative measures- comparing data sets

62

## sampling distribution

### probability distribution of all values for an estimate that we might obtain when we sample a population, centred at true µ

63

## values outside of CI

### implausible

64

## how many quadrats to use

### till cumulative number of observations asymptotes

65

## law of total probability

###
P[A] = Σ P[B].P[A I B]

for all B_i 's

66

## null distribution

### sampling distribution for test statistic, if repeated trials many time and graphed test statistics for H_o

67

## Type I error

### P[Reject Ho I Ho true] = alpha

68

## reject null

### P-vale < alpha

69

## Type II error

### P[do not reject Ho I Ho false]

70

## Power

###
P[Reject Ho I Ho false]

increases with large n

decreases P[Type II E]

71

## test statistic

### used to evaluate whether data are reasonably expected under Ho

72

## p-value

### probability of getting data as extreme or more, given Ho is true

73

## statistically significant

###
data differ from H_o

not necessarily important- depends on magnitude of difference and n

74

## why not reduce alpha

### would decrease P[Type I] but increase P[Type II]

75

##
continuous probability

P[Y = y] =

### 0

76

## sampling without replacement

###
ex. drawing cards

(1/52).(1/51).(1/50)

77

## Bayes Theorem

### P[A I B] = ΣP[B I A].P[A] / P[B]

78

## P-value > alpha

###
do not reject Ho

data are consistent with Ho

79

## meaning of 'z' in standardization

### how many sd's Y is from µ

80

## standardization for sample mean, t =

### Ybar - µ / (s / sq.rt. n)

81

## CI on µ

###
Ybar ± SE.tcrit

SE of Ybar

t of alpha(1 or 2), degrees of freedom

82

## 1 sample t-test

### compares sample mean from normal pop. to population µ proposed by Ho

83

## why n-1 account for sampling error

### last value is not free to vary if mean is a specified value

84

## 1 sample t-test assumptions

###
data are a random sample

variable is normally distributed in pop.

85

## paired t-test assumptions

###
pairs are a random sample from pop.

paired differences are normally distributed in the pop.

86

## how to tell whether to reject with t-test

### if test statistic is further into tails than critical t then reject

87

## 2 sample design compares

### treatment vs. control

88

## 2 sample t-test assumptions

###
both samples are random samples

variable is normally distributed in each group

standard deviation in two groups ± equal

89

## degrees of freedom

###
1 sample t-test: n - 1

paired t-test: n - 1

2 sample t-test: n1 + n2 - 2

90