(M) Data Analysis Part 2 Flashcards

1
Q

Existing criterion “truth” based on the gold standard

A

Validity

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2
Q
  1. Is the percentage of people with the disease who are detected by the test
  2. Is the percentage of people with the disease who are not detected by the test
  3. Is the percentage of people with the disease who are correctly labelled by the test as not disease

A. PREDICTIVE VALUE OF A POSITIVE TEST
B. %FALSE NEGATIVE
C. %SENSITIVITY
D. %SPECIFICITY

A

CBD

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3
Q
  1. Is the percentage of people without the disease who are incorrectly labelled by the test as having disease
  2. Is defined as the likelihood that an individual with a positive test has the disease
  3. Is defined as the likelihood that a person with a negative test does not have the disease

A. PREDICTIVE VALUE OF A NEGATIVE TEST
B. PREDICTIVE VALUE OF A POSITIVE TEST
C. %FALSE POSITIVE

A

CBA

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4
Q

pa-study nalang ng formula, thanks

A

oks

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5
Q

The ratio of the chance of the test being positive if having the condition compared to the chance of testing positive if not having the condition.

A

Positive Likelihood Ratio +LR

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6
Q

The ratio of the chance of the test being negative if having the condition compared to the chance of testing negative in not having the condition.

A

Negative Likelihood ratio -LR

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7
Q

● Also termed as “reproducibility” or “repeatability”
● Refers to the stability or consistency of information
● The extent to which similar information is supplied when
measurements are performed more than once

A

RELIABILITY

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8
Q

A key goal in applied biostatistics is to make inferences about unknown population parameters based on sample statistics

A

INFERENTIAL STATISTICS

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9
Q
  • a range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter.
  • Because of their random nature, it is unlikely that two samples from a particular population will yield identical confidence intervals
  • But if you repeated your sample many times, a certain percentage of the resulting confidence intervals would contain the unknown population parameter.
A

Confidence interval

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10
Q
  1. ratio or interval form of measurement
  2. nominal scale of measurement;
    categorized
  3. studying one population but comparing it to a gold standard

A. One Sample
B. Dichotomous Variable
C. Continuous Variable

A

CBA

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11
Q
A
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11
Q

two types of estimates for each population
parameter:

A

○ Point estimate
○ Confidence interval (CI) estimate.

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12
Q

a range of likely
values for the population parameter based on:
○ The point estimate, e.g., the sample mean
○ The investigator’s desired level of confidence (most commonly 95%, but any level between 0-100% can be selected)

A

confidence interval estimate (CI)

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13
Q

With smaller samples (n< 30) the Central Limit Theorem does not apply, and another distribution called the _______________ must be used

A

t distribution

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14
Q
A
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15
Q

similar to the standard normal
distribution but takes a slightly different shape depending on
the sample size.

A

t distribution

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16
Q

t values are listed by

A

degrees of freedom (df)

17
Q

a contention or assumption made concerning a population’s characteristics. It is usually concerned with the parameters of the population about which the statement is made

A

Statistical Hypothesis

18
Q
  • The purpose of the research is to provide evidence to support or refute the null hypothesis
  • Comprises a set of procedures.
  • either rejected or not based on the probability of occurrence of the sample results if the null
    hypothesis were true
A

Hypothesis in testing

imma skip this part, alam niyo na ‘yan

19
Q

Include values of statistics leading to rejection of null
hypothesis.

A

CRITICAL REGION OR REGION OF REJECTION

20
Q

Include values of statistics leading to rejection of null hypothesis. Usually called

These values are those whose probability of occurrence is less than or equal to the level of significance, α.

A

alpha or tail of the curve.

21
Q

Satatistical tests

What statistical test should be used to compare a single population with interval/ratio data?

A

Z Test or T test

22
Q

What statistical test should be used to compare a single population with ordinal data?

A

Kolmogorov
Smirnov one
sample test

23
Q

What statistical test should be used to compare a single population with nominal data?

A

Z Test
Chi square Test

24
What statistical test should be used to compare a two population with interval/ratio data for related samples?
Paired t Test
25
What statistical test should be used to compare a two population with ordinal data for related samples?
Wilcoxon matched pairs signed ranks test
26
What statistical test should be used to compare a two population with nominal data for related samples?
McNemar's Test
27
What statistical test should be used to compare a two population with interval/ratio data for indepdent samples?
Independent t Test
28
What statistical test should be used to compare a two population with ordinal data for indepdent samples?
Mann Whitney U Test
29
What statistical test should be used to compare a two population with nominal data for indepdent samples?
* Fisher’s Exact Probability Test * Chi square Test
30
What statistical test should be used to compare a three or more population with interval/ratio data for related samples?
F-test: Two way Analysis of Variance
31
What statistical test should be used to compare a three or more population with ordinal data for related samples?
Friedman’s Analysis of Variance
32
What statistical test should be used to compare a three or more population with nominal data for related samples?
Cochran’s Q Test
33
What statistical test should be used to compare a three or more population with interval/ratio data for independent samples?
F-test: One way ANOVA
34
What statistical test should be used to compare a three or more population with ordinal data for independent samples?
Kruskall Wallis one way ANOVA
35
What statistical test should be used to compare a three or more population with nominal data for independent samples?
Chi square Test
36
Study of Relationship between Variables: Interval ratio
Regression Correlation
37
Study of Relationship between Variables: Ordinal
Spearman rank correlation coefficient
38
Study of Relationship between Variables: Nominal
Kappa Test Contingency coefficient test
39
degree of prediction
REGRESSION STUDY