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FCM - Evidence Based Medicine > 2 - Biostatistics 1 - Basic Principles > Flashcards

2 - Biostatistics 1 - Basic Principles Flashcards

1
Q

Statistics

A

Encompasses methods of collecting, summarizing, analyzing & drawing conclusions from data

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2
Q

Biostatistics

A

The application of statistics to medical, biological and public health data

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3
Q

Descriptive Statistics

A

A means of organizing and summarizing observations

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4
Q

Statistical Inference

A

A process of drawing conclusions about a population from a sample

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5
Q

Population

A

A collection of all subjects of interest

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6
Q

Sample

A

A representative subset of the population that can be studied

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7
Q

Types of Sample

A

Random (every 10th person)

Convenient (this cluster all together)

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8
Q

Parameter

A

Rule (applicable to population)

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9
Q

Statistic

A

Value (measured from sample)

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10
Q

Variable

A

A characteristic or condition of an observation that can take on different values

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11
Q

Dependent Variable

A

Outcome, variable of interest

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12
Q

Independent Variable

A

Exposure, predictor variables

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13
Q

Types of Categorical (Qualitative) Data

A

Nominal

Ordinal

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14
Q

Nominal Data

A

Values fall into categories of classes that are mutually exclusive and are not ordered
Dichotomous/Binary - Only Two Possible Categories (Dead/Alive)
Multiple Categories - (Race, Blood Type)

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15
Q

Ordinal Data

A

Values fall into categories or classes where order matters (Disease stage, satisfaction level)

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16
Q

2 Types of Numerical (Quantitative) Data

A

Discrete

Continuous

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17
Q

Discrete Data

A

Data has a numerical value that takes only certain whole number values (# of kids in a family)

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18
Q

Continuous

A

Data has a numerical value that can have any value in a continuum (height, weight, time)

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19
Q

Frequency Distributions - Data Representations

A

Categorical Data - Pie Charts, Bar Charts
Continuous Data - Histogram
Continuous Data - Box Plot

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20
Q

Unimodal Frequency Distribution

A

One Peak

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21
Q

Bimodal Frequency Distribution

A

Two Peaks

22
Q

Right Skew Frequency Distribution

A

Tail to the right (more low values than high values)

23
Q

Left Skew Frequency Distribution

A

Tail to the left (more high values than low values)

24
Q

Central Tendency Descriptors

A

Mean
Median
Mode

25
Q

Mean

A

Average
Pro - Uses all Data Values
Con - Distorted by outliers and skewed data

26
Q

Median

A

Middle value of the ordered data set
Pro - Not distorted by outliers or skewed data
Con - Ignores most of the information

27
Q

Mode

A

Most frequently occurring value
Pro - Easily determined for categorical data
Con - Ignores most of the information

28
Q

Spread

A 
Measures to describe the variability of dispersion
Range
IQR
Variance
Standard Deviation
29
Q

Range

A

Difference between largest and smallest values
Pro - Easily Determined
Con - Distorted by Outliers

30
Q

IQR (Inter-Quartile Range)

A

Difference between the 25th and 75th percentiles
Pro - Unaffected by outliers
Con - Appropriate for skewed data

31
Q

Variance

A

Each deviation is squared

32
Q

Standard Deviation

A

Square root of variance, an average of deviations from the observations from the mean

33
Q

Inferential Statistics

A

The process of drawing conclusions about a population from a sample.
Starts with a Null Hypothesis and an Alternative Hypothesis

34
Q

Null Hypothesis (H0)

A

Assumes no effect in the population

35
Q

Alternative Hypothesis (H1)

A

Assumes effect in the population

36
Q

Steps for Hypothesis Testing in Inferential Statistics

A

Assume Null Hypothesis to be true
Collect data from the sample to disprove Null Hypothesis
Either reject H0 (if there is convincing/strong evidence against it) or fail to reject H0

37
Q

Type 1 Error

A

Reject the null when the null is actually true

Probability - α

38
Q

Type 2 Error

A

Fail to reject the null when the null is false

Probability - β

39
Q

Power

A

The probability of rejecting H0 when it is false (Not committing a Type 2 error) = 1 - β
Aim for 100%, settle for 80 - 90%

40
Q

Factors influencing power

A

Sample Size
Variability
Effect of Interest
Significance Level

41
Q

How does Sample Size influence Power?

A

Power increases with larger samples

42
Q

How does Variability influence Power?

A

Power increases as variability decreases

43
Q

How does Effect of Interest influence Power?

A

Power increases with larger effect size

44
Q

How does Significance Level influence Power?

A

Power increases with larger α

45
Q

α

A

The chance of Type 1 Error we are willing to accept, decided prior to collecting data
Typically α = 0.05
Using a smaller α will increase your β

46
Q

P-Value

A

The probability of obtaining our results or something more extreme given that the null hypothesis is true

47
Q

P

A

Reject H0 and conclude that results are significant at the α% level

48
Q

Confidence Interval

A

Estimated range of values likely to include the population parameter.
Point estimate, 95% CI (upper limit, lower limit)

49
Q

What do P-Values tell you about?

A

Statistical Significance

50
Q

What do Confidence Intervals tell you?

A

Statistical Significance + Information about Size and Direction of the effect.

51
Q

Statistical Significance

A

90% Confidence Interval does not include the null

A very small difference that is not clinically meaningful can reach statistical significance if the sample size is large enough

52
Q

Clinical Significance

A

Effect Estimate is above the threshold for clinical relevance

FCM - Evidence Based Medicine (11 decks)

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