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BIOSTAT > INFERENTIAL STATS > Flashcards

INFERENTIAL STATS Flashcards

1
Q

consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

A

Inferential statistics

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2
Q

statistician tries to make inferences from samples to populations.

A

Inferential statistics

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3
Q

Inferential statistics uses ___________—, i.e., the chance of an event occurring.

A

probability

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4
Q

A ___________________ consists of all subjects (human or otherwise) that are being studied.

A

population

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5
Q

Most of the time, due to the expense, time, size of population, medical concerns, etc., it is not possible to use the entire population for a statistical study; therefore, researchers
use ________

A

samples

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6
Q

__________ is a group of subjects selected from a population.

A

sample

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7
Q

An area of ____________ called hypothesis testing is a decision-making
process for evaluating claims about a population, based on information obtained from samples.

A

inferential statistics

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8
Q

__________________ includes making inferences from samples to populations,
estimations and hypothesis testing, determining relationships, and making
predictions.

A

Inferential statistics

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9
Q

Inferential statistics is based on _______________

A

probability theory

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10
Q

One aspect of inferential statistics is __________, which is the process of estimating the value of a parameter from information obtained from a sample.

A

estimation

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11
Q

Inferential statistical techniques have various ______________that must be met before valid conclusions can be obtained.

A

assumptions

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12
Q

Some statistical techniques are called _______. This means that the distribution of the variable can depart somewhat from normality, and valid conclusions can still be obtained

A

robust

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13
Q

A continuous, symmetric, bell-shaped distribution of a variable

A

Normal DIstribution

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14
Q

If a random variable has a probability distribution whose graph is continuous, bell-shaped, or symmetric, it is called a ____________________

A

normal distribution.

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15
Q

was named after the German mathematician Carl Friedrich Gauss.

A

Bell curve or Gaussian Distribution

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16
Q

A normal distribution curve is _____-shaped

A

bell

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17
Q

IN A NORMAL DISTRIBUTION, The ______________________- are equal and are located at the center of the distribution.

A

mean, median, and mode

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18
Q

A normal distribution curve is ______ (i.e., it has only one mode).

A

unimodal

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19
Q

IN A NORMAL DISTRIBUTION, The curve is symmetric about the _____, which is equivalent to saying that its shape is the same on both sides of a vertical line passing through the center.

A

mean

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20
Q

IN A NORMAL DISTRIBUTION, The curve is _________; that is, there are no gaps or holes. For each value of X, there is a corresponding value of Y.

A

continuous

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21
Q

IN A NORMAL DISTRIBUTION, The curve never touches the _______. Theoretically, no matter how far in either direction the curve extends, it never meets the ______ but it gets increasingly closer.

A

x axis

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22
Q

IN A NORMAL DISTRIBUTION, The total area under a normal distribution curve is equal to __________

A

1.00, or 100%

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23
Q

The area under the part of a normal curve that lies within 1 standard deviation of the mean is approximately _________

A

0.68, or 68%

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24
Q

The area under the part of a normal curve that lies within
2 standard deviations, about __________

A

0.95, or 95%

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25
Q

The area under the part of a normal curve that lies within
3 standard deviations, about __________

A

0.997, or 99.7%

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26
Q

3 TYPES OF DISTRIBUTION

A

Symmetric Distribution
Negatively/Left-Skewed Distribution
Positively/Right-Skewed Distribution

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27
Q
  • the data values are evenly distributed about the mean
A

Symmetric Distribution

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28
Q

– majority of the data falls to the right of the mean

A

Negatively/Left-Skewed Distribution

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29
Q

– majority of the data falls to the left of the mean

A

Positively/Right-Skewed Distribution

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30
Q

A normal distribution with a mean of __ and a standard deviation of __

A

0 AND 1

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31
Q

Suppose a college president wishes to estimate the average age of students attending this semester. The president could select a random sample of 100 students and find the average age of these students, say 22.3 years (this is an example of a ___________)

A

point estimate

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32
Q

A specific numerical value estimate of a parameter.

A

Point estimate

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33
Q

The best point estimate of the population mean µ is the _____________.

A

sample mean X

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34
Q

There isn’t really a way of knowing how close a particular point estimate is to the population mean. That’s why most statisticians prefer another estimate which is the ____________

A

interval estimate

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35
Q

An interval or a range value is used to estimate the parameter.

A

Interval estimate

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36
Q

This estimate may or may not contain the value of the parameter being estimated

A

Interval estimate

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37
Q

If the sample size is >30, the distribution of the means will be approximately ______

A

normal

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38
Q

3 properties of a good estimator:
The estimator should be an ___________. That is, the expected value or the mean of the estimates obtained from samples of a given size is equal to the parameter being estimated.

A

unbiased estimator

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39
Q

The estimator should be _________. For a ___________ estimator, as the sample size increases, the value of the estimator approaches the value of the parameter estimated.

A

consistent

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40
Q

The estimator should be a ______________________-. That is, of all the statistics that can be used to estimate a parameter, the ____________________ has the smallest variance.

A

relatively efficient estimator

41
Q

– is the percentage of times you expect to get close to the sample estimate if you’re going to rerun the experiment again.

A

Confidence level

42
Q

Three common confidence levels:

A

90%, 95%, 99%

43
Q

Critical value of 90%

A

± 1.65

44
Q

Critical value of 95%

A

± 1.96

45
Q

Critical value of 99%

A

± 2.58

46
Q

α value of 90%

A

0.10

47
Q

α value of 95%

A

0.05

48
Q

α value of 99%

A

0.01

49
Q

The _____________ is a statistic expressing the amount of allowable random sampling error in results

A

margin of error

50
Q

The larger the ___________, the less confidence that the sample results are close to the “true” figures for the whole population

A

margin of error

51
Q

The act of choosing the number of observations or replicates to include in a statistical sample

A

SAMPLE SIZE DETERMINATION

52
Q

used in any empirical study to make inferences about a population from a sample

A

SAMPLE SIZE DETERMINATION

53
Q

3 METHODS OF HYPOTHESIS TESTING

A

The classical approach
The P-value approach
The confidence interval approach

54
Q

If the sample value observed is too many standard deviations away from the true value claimed under H0, then it must be too unlikely H0, is true

A

The classical approach

55
Q

If the probability of the sample value being that far away is small, then it must be too unlikely H0, is true

A

The P-value approach

56
Q

If we are not sufficiently confident that the parameter is likely enough, then it must be too unlikely

A

The confidence interval approach

57
Q

Every hypothesis-testing situation begins with the __________

A

statement of a hypothesis

58
Q

A ____________ is a conjecture about a population parameter.

A

statistical hypothesis

59
Q

The _____________ symbolized by H0 is a statistical hypothesis that states that there is no difference between a parameter and a specific value, or that there is no difference between two parameters.

A

null hypothesis

60
Q

The _______________, symbolized by H1 is a statistical hypothesis that states the existence of a difference between a parameter and a specific value or states that there is a difference between two parameters.

A

alternative hypothesis

61
Q

____________ – critical area is two sided and tests whether a sample is > or < a certain range of values

A

Two-tailed test

62
Q

____________ = if interest is in the increase only

A

One-tailed test (right)

63
Q

__________(can be rejected based on statistical evidence)
Always stated with “equals” sign representing a given value

A

Null hypothesis

64
Q

____________ (can be used to support a claim)
Sometimes known as the research hypothesis

A

Alternative hypothesis

65
Q

Alternative hypothesis is also known as the

A

research hypothesis

66
Q

A type __ error occurs if you reject the null hypothesis when it is true

A

I

67
Q

A type ___ error occurs if you do not reject the null hypothesis when it is false

A

II

68
Q

type II error occurs if

A

you do not reject the null hypothesis when it is false

69
Q

A type I error occurs if

A

you reject the null hypothesis when it is true

70
Q

α value - 0.10
Level of Significance

A

10% chance of rejecting a true null hypothesis

71
Q

α value - 0.05
Level of Significance

A

5% chance of rejecting a true null hypothesis

72
Q

α value - 0.01
Level of Significance

A

1% chance of rejecting a true null hypothesis

73
Q

The _______________- is the maximum probability of committing a type I error. This probability is symbolized by a (Greek letter alpha). That is P(type I error) = α.

A

level of significance

74
Q

The ______________ separates the critical region from the noncritical region.

A

critical value

75
Q

The ____________________ is the range of values of the test value that indicates that there is a significant difference and that the null hypothesis should be rejected.

A

critical or rejection region

76
Q

The ___________________ is the range of values of the test value that indicates that the difference was probably due to chance and that the null hypothesis should not be rejected

A

noncritical or nonrejection region

77
Q

A ____________ indicates that the null hypothesis should be rejected when the test value is in the critical region on one side of the mean.

A

one-tailed test

78
Q

Conjuncture about a population parameter.
This conjecture may or may not be true

A

STATISTICAL HYPOTHESIS

79
Q

There is no difference between 2 parameters

A

Null Hypothesis (H0)

80
Q

There is a difference between 2 parameters

A

Alternative Hypothesis (H1)

81
Q

HYPOTHESIS TESTING AND CRITICAL
VALUES

A
  1. State the hypothesis
  2. Select the statistical test
  3. Choose the level of significance
  4. Formulate a plan for study
  5. Analyze results and make a decision
82
Q

a statistical test for the mean of a population and it is used when:
* n is greater than or equal to 30
* when the population is normally distributed

A

Z TEST FOR MEAN

83
Q

a statistical test for the mean of a population and it is used when the population is normally or approximately normally distributed or is unknown

A

T TEST FOR MEAN

84
Q

If σ is KNOWN and n > 30, use the

A

z-test.

85
Q

If σ is KNOWN, and n < 30, use the

A

t-test.

86
Q

If σ is UNKNOWN, but n > 30, use the

A

t-test.

87
Q

If σ is UNKNOWN, and n < 30, use the

A

t-test.

88
Q

probability of getting a sample statistic such as the mean or a more extreme sample statistic in the direction of the alternative hypothesis when the null hypothesis is true

A

Z TEST FOR MEAN

89
Q

p > 0.10

A

weak or no evidence

90
Q

0.05 < p ≤ 0.10

A

Moderate evidence

91
Q

0.01 < p ≤ 0.05

A

Strong evidence

92
Q

p ≤ 0.01

A

Very strong evidence

93
Q

DECISION RULE USING A P-Value
if p value <= alpha value

A

reject the null hypothesis

94
Q

DECISION RULE USING A P-Value
if p value => alpha value

A

do not reject the null hypothesis

95
Q

DECISION RULE USING A P-Value
if p value <= 0.01

A

reject null hypothesis. Difference is
highly significant

96
Q

DECISION RULE USING A P-Value
0.01 < p < = 0.05

A

reject null hypothesis. Difference is
significant

97
Q

DECISION RULE USING A P-Value
if 0.05 < p <=0.10

A

consider consequences of type 1
error before reject null hypothesis

98
Q

DECISION RULE USING A P-Value
if p > 0.10

A

Do not reject the null hypothesis.
Result is significant

BIOSTAT (25 decks)

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