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Biostatistics > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

Descriptive Statistics

A

Organizing and summarizing data using numbers and graphs.
Data that we collect or observe (empirical data).
Examples- frequencies and associated percentages, average rank of outcome, pie charts, bar graphs, or other visual representations of data.

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2
Q

Inferential Statistics

A

A range of procedures/ statistical tests (t-test, chi-square, multiple regression analysis).
Allows us to generalize from our sample of data to a larger group of subjects.
Examples: population- parameter mean, standard deviation
Examples: sample- statistics (standard deviation)

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3
Q

Discrete variable

A

Discrete variable is characterized by gaps or interruptions.
Referred to as “qualitative data”
They have values that can only assume whole numbers. They can have only one of a limited set of values.
Example- sex, marital status, blood types

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4
Q

Continuous variable

A

Has no gaps or interruptions. It may take any value within a reference range.
Referred to as “Quantitative data”
Examples- height, weight, BP, final exam scores

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5
Q

Dependent variable

A

Y
The outcome of interest, which should change in response to some interventions
For example- Final exam score for educational assessment, blood glucose levels for diabetic tests, bio-availability measurement (Cmax)

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6
Q

Independent variable

A

The intervention, or what is being manipulated. A variable keeps changing its value. It allows us to control some of the research environment.
Predictor variables
Example- temperature levels for tested animals, experimental vs control drug therapy groups, institutional vs community pharmacy.

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7
Q

What are the 4 types of scales for measurement

A

Nominal, ordinal, interval, ratio

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8
Q

Nominal scale

A

Named categories with no implied order among the categories
Attributes are only named, weakest scale
Examples- Sex (male, female), Race, Most percentages, yes/no data

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9
Q

Ordinal Scale

A

Same as nominal, but includes ordered categories. The difference between these categories cannot be considered equal.
Examples- Letter grades, heath outcome, satisfaction scores, agreement levels, rankings, scales, rates, medical conditions

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10
Q

Interval Scale

A

Same as ordinal plus equal intervals. Data has equal distances between scores, but the zero point is arbitrary.
Example- BP, Time, temperature, lab values

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11
Q

Ratio Scale

A

Data has equal intervals between points and a meaningful zero point.
Example- BP, Time, temperature, lab values

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12
Q

Population samples

A

Represent the best estimate we have of the true parameters of that population.

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13
Q

Random sampling

A

Equal chance of being included in the sample.

Example- use of random numbers table

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14
Q

Stratified Sampling

A

Population is divided into subgroups (strata) with similar characteristics, then randomly select samples from each strata.
Example- Age groups (age 0-18, age 19-34. etc.)

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15
Q

Selective Sampling

A

Not random sampling, convenient sampling

Example- select all patients who visited the psychiatric clinic today

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16
Q

Cluster Sampling

A

Many individual “primary” units that are clustered together in secondary (units can be sub-sampled)
Example- For Q/C sampling, randomly select 10 bottles (secondary) then select 4 tablets (primary) from the top, middle, and bottom of the bottle.

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17
Q

Systematic Sampling

A

Systematically select subjects as sample.

Example- Every 9th person will be selected.

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18
Q

What is the difference between cluster and stratified sampling?

A

Stratified sampling seeks to divide the sample into heterogeneous groups so the variance within the strata is low and between the strata are high.
Cluster sampling seeks to have each cluster reflect the variance in the population. Each cluster is a “mini” population.

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19
Q

Symmetric (bell-shaped) distribution

A

Mean=median=mode

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20
Q

Positively skewed distribution

A

Mode

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21
Q

Negatively skewed distribution

A

Mode>median>mean

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22
Q

Why is degree of freedom used?

A

To correct for the bias in the results that would occur if just n was used
n-1

23
Q

Variance

A

The average variability of scores in the distribution measured in squared units of the original score

24
Q

What is the population variance?

A

The average squared deviation of scores about their mean.

25
Q

Which is the property of normal distribution?
A,) Unimodal with continuous data
B.) Mean, median, mode are all equal
C.) Sum of probability of all possible outcomes is equal to 1.0
D.) All of the above

A

D

26
Q

For normal distribution, what happens to the standard deviation as the sample size increases?

A

It decreases

27
Q

Does inferential statistics use sample or population?

A

Sample

28
Q

What is the difference between sample and population?

A

Sample- those individuals in the study. A sample deals with statistics
Population- the hypothetical (and usually) infinite number of people to whom you wish to generalize. A population deals with parameter

29
Q

Standard error of mean (S.E or SEM)

A

Represents the possible variability of the mean itself.
S.E shows how close the man scores from repeated samples will be to the true population mean
Equal to the population standard deviation divided by the square root of the sample size.

30
Q

Null hypothesis

A

The hypothesis to be tested
There is no difference between 2 population means
Hypothesis A

31
Q

Alternative hypothesis

A

Set of hypothesis that remain tenable when the null hypothesis is rejected.
Hypothesis A is false
There is a difference between “sample” and “population”

32
Q

Level of significance testing

A 
Does a particular sample belong to a hypothesized population?
Steps:
1.) Null and alternative hypothesis
2.) alpha level (commonly 5% or 0.05)
3.) p value
33
Q

P value

A

Probability of observing what you observed base don the study sample data
If the p value is less than the predetermined alpha value, we reject the null hypothesis.

34
Q

Statistical Power

A

Power is the ability of a statistical test to show if a significant difference truly exists.
Termed 1-beta (1-b)

35
Q

Type I error

A

Rejecting the null hypothesis when it is actually true
alpha level determines the chance for type I error
Example- sending an innocent person to prison

36
Q

Type II error

A

Accepting the null hypothesis when it is actually false.
Beta, B is the probability of concluding there was no difference when, in fact, there was one.
Freeing a guilty person

37
Q

What happens when the null hypothesis is true and you accept it?

A

Correct

1-alpha

38
Q

What happens when the null hypothesis is false and you accept it

A

Type II error

beta

39
Q

What happens when the null hypothesis is true and you reject it

A

Type I error

alpha

40
Q

What happens when the null hypothesis is false and you reject it?

A

Correct

Power= 1-beta

41
Q

Standard Scores (Z score)

A

Z score is number of standard deviation above or below the mean for a particular score

42
Q

Z tests

A

Involve a dependent variable. Useful when comparing a sample statistic to a known population parameter (mean and SD).
Accurate only for large samples

43
Q

Z test steps

A

1.) Make the null hypothesis
2.) State the alternative hypothesis
3.) State a decision rule
alpha-0.05, 95% confidence interval; critical value=1.96
4.) Compute relevant z statistics
5.) Evaluate the null hypothesis

44
Q

Confidence Intervals

A

The result of estimate is a range of possible outcomes defined as boundary values or confidence limits.

45
Q

95% confidence interval

A

z=1.96; alpha=0.05
95% chance that the true population mean falls within the range. There is a 5% chance that the truth is outside the range.

46
Q

68% C.I

A

z=1

47
Q

96% C.I

A

z=2

48
Q

98% C.I

A

z=2.58

49
Q

As sample size increases, what happens to CI?

A

The width of CI decreases

50
Q

Two-tailed test

A

Nondirectional test u1=u2

51
Q

One-tailed test

A

Directional u>20

52
Q

What are t tests used for?

A

Comparing one sample to a known population parameter

Comparing 2 samples to each other and making inferences to their population

53
Q

What are t-tables used for?

A

To adjust the z-values of a normal distribution to account for sample sizes

54
Q

One-sample t test

A

Can be used to either estimate the population mean or compare the sample mean to an expected population mean

Biostatistics (2 decks)

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