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Research Evaluation > Lecture 5 - Biostatistics Part 1-2 > Flashcards

Lecture 5 - Biostatistics Part 1-2 Flashcards

1
Q

What are the Measures of Central Tendency?

A

Mean - the “average” – sum of the set divided by the number in the set

Median – the middle point (arrange the data smallest to largest, then find the middle point)

Mode – the score that occurs most frequently in a set of data
—-May have two most common values = “bimodal distribution”

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2
Q

the “average” – sum of the set divided by the number in the set

A

Mean -

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3
Q

– the middle point (arrange the data smallest to largest, then find the middle point)

A

Median

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4
Q

– the score that occurs most frequently in a set of data

A

Mode

—-May have two most common values = “bimodal distribution”

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5
Q

Mode

—-May have two most common values =

A

“bimodal distribution”

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6
Q
  • The point/score at which 50% of scores fall below it and 50% fall above it
A

Median

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7
Q

This is the most general and least precise measure of central tendency

When two values occur the same number of times – Bimodal distribution

A

Mode - most frequently occurring value

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8
Q

look at mean median mode images on slide 10

A

!

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9
Q

Standard deviation

A

a measure of variation of scores about the mean

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10
Q

Variance compared to…

A

standard deviation

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11
Q

quantifies the amount of variability, or spread, around the mean of the measurements.

To calculate: take each difference from the mean, square it, and then average the result

A

Variance (σ2 )

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12
Q

To calculate the variance:

A

take each difference from the mean, square it, and then average the result

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13
Q

: a measure of variation of scores about the mean

A

Standard deviation (σ)

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14
Q

To calculate the standard deviation:

A

take the √ of the variance
(the “average distance” to the mean)

In practice, the standard deviation is used more frequently than the variance.

Primarily because the standard deviation has the same units as the measurements of the mean.

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15
Q

When comparing two groups, the group with the larger standard deviation exhibits a greater amount of _____ while the groups with smaller deviation has less _______.

A

variability (heterogeneous)

variability (homogeneous)

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16
Q

Empirical rule for data (68-95-99) - only applies to a set of data having a distribution that is approximately bell-shaped:

A

Approximately 68% of all scores fall with 1 standard deviation of the mean

Approximately 95% of all scores fall with 2 standard deviations of the mean

Approximately 99.7% of all scores fall with 3 standard deviations of the mean

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17
Q

Scatterplots:

A useful summary of a set of ________-

A

bivariate data (two continuous variables)

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18
Q

Scatterplots:

Gives a good visual picture of the relationship between the two variables, and aids the interpretation of the _____

A

correlation coefficient or regression model.

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19
Q

the statistic that summarizes the relationship between the variable on the x axis and the variable on the y axis

A

correlation coefficient

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20
Q

Perfect Positive correlation

A

X increases and Y increases at the same rate

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21
Q

Perfect Negative correlation

A

X increases and Y decreases yet at the same rate

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22
Q

X increases and Y increases =

A

positive correlation

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23
Q

X increases and Y decreases =

A

negative correlation

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24
Q

0 value for correlation coefficient means there is

A

no correlation

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25
Q

Represented by “r ” (rho)

The absolute value of the coefficient (its size, not its sign) tells you how strong the relationship is between the variables.

A

Correlation Coefficient

26
Q

Tells us how strongly two variables are related

A

Correlation Coefficient

27
Q

Correlation Coefficient

“r” can not be > 1 or < -1
Closer to -1 or +1: ?
Closer to 0 : ?

A

Closer to -1 or +1: the stronger the relationship

Closer to 0 : the weaker the relationship

28
Q

The most common measure of association. Results can misleading if the relationship is non-linear.

A

Pearson Correlation

29
Q

______ correlation is very sensitive to outlying values.

A

Pearson’s

30
Q

______ non-parametric version of Pearson’s correlation. The calculation is based on the ranks of the data points of the x and y values.

A

Spearman Correlation:

31
Q

The statement that establishes a relationship between variables being assessed
Example: In a clinical trial the hypothesis states the new drug is better the placebo

A

Alternative hypothesis (Ha or H1)

32
Q

The statement of no difference or no relationship between the variables

Example: In a clinical drug trial the null hypothesis states that the new drug is no better than placebo

A

Null hypothesis (Ho)

33
Q

AKA the “research hypothesis”

A

Alternative hypothesis

34
Q

Hypothesis stating the expected relationship between independent and dependent variables.

A

Alternative hypothesis

35
Q

If there IS a statistically significant difference, then the researchers “ACCEPT” the alternative hypothesis and “REJECT” the null hypothesis.

A

Alternative hypothesis

36
Q

If there IS a statistically significant difference, then the researchers must “REJECT” the Null hypothesis

A

Null hypothesis

37
Q

If there IS NOT a statically significant difference, then the researchers must “RETAIN” or “FAIL to REJECT” the Null hypothesis.

A

Null hypothesis

38
Q

Two kinds of errors can be made when we conduct a test of hypothesis.

A

This first is called a Type I error; also known as a rejection error or an α error.

The second kind of error that can be made during a hypothesis test is a Type II error, also known as an acceptance error or an β error.

39
Q

A _____ error is made if we reject the null hypothesis when null hypothesis is true.

A

type I

40
Q

The probability of make a type I error is determined by the ______ of the test.

A

significance level

41
Q

The second kind of error that can be made during a hypothesis test is a Type II error, also known as an _____ error or an _____ error.

A

acceptance error or β error

42
Q

A Type 2 error is made if we….

A

…fail to reject null hypothesis.

43
Q

The probability of committing a type II error is represented by…

A

the Greek letter β.

44
Q

The probability of finding an effect
The probability of correctly rejecting the null hypothesis
The probability of seeing a true effect if one exists
Designers of studies typically aim for a power of 80% or 0.8
Implies there is an 80% chance of getting it right
Generally speaking: More people = more power

A

Statistical power

45
Q

The probability of correctly rejecting the null hypothesis

A

Statistical power

46
Q

The probability of seeing a true effect if one exists

A

Statistical power

47
Q

Designers of studies typically aim for a power of 80% or 0.8
Implies there is an 80% chance of getting it right

A

Statistical power

48
Q

______ calculates the number of participants a study must have to draw accurate conclusions

Takes into consideration: estimated effect size, sample means, etc.

A

power analysis

49
Q

what does a power analysis take into consideration?

A

Takes into consideration: estimated effect size, sample means, etc.

50
Q

is the probability of avoiding a type II error.

A

Power

51
Q

Power may also be thought of as the likelihood that a particular study will detect a _____________ given that one exists.

A

deviation from the null hypothesis

52
Q

If the β is the probability of making a type II error, 1- β is called the….

A

power of the test of hypothesis.

53
Q

1- β is called….

A

the power of the test of hypothesis.

54
Q

Statistical significance and p-value

How do we determine if the study result happened by chance alone?
What determines statistical “significance”?

A

Significance
The probability of rejecting a true H0

α = .05 usually set, acceptable error

—–Chance that 5 times out of 100 the H0 would be falsely rejected

55
Q

Statistical significance and p-value

A

Probability level

The likelihood that the difference observed between two interventions could have arisen by chance

Accepted value is 5% risk (p = .05)

Means there is a 5% chance that the results happened by chance

Allows us to reject or accept the null hypothesis

56
Q

Statistical significance and p-value

p is…

α is the acceptable error, usually = .05

p ≤ α

_____ the H0 (null hypothesis)
the results…

p > α

_____ the H0 (null hypothesis)
the results….

A

the chance of random error

α is the acceptable error, usually = .05
p ≤ α

reject the H0 (null hypothesis)
the results are statistically significant

p > α
fail reject the H0 (null hypothesis)
the results not statistically significant

57
Q

Less than 5% chance the effect happened by chance.

“There is less than a 5% chance that the null hypothesis is falsely rejected”

Often reported as “significant at the 0.05 level.”

A

A statistically significant p = or < .05

58
Q

If the number of subjects is small, p value tells us that the effect was either large or consistent (or both)

If the number of subjects is large, the effect size may not be that large

A

A highly significant p < .001

59
Q

If the number of subjects is small, there might not have been enough subjects to find a difference that truly does exist

If the number of subjects is large, we can be confident that either there is no difference between treatments, or the treatment effect is not consistent

A

An insignificant p > .05

60
Q

Statistical significance and p-value

Depends on several factors…

As all of these factors increase, the likelihood of finding statistical significance increases

A

How large the effect was

How consistent the effect was

How many patients were studied

—As all of these factors increase, the likelihood of finding statistical significance increases

61
Q

What’s the difference between….

P value of .051 and .049

A

Look at the slides if you don’t know for sure…. slide 28 in particular

Research Evaluation (12 decks)

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