# BEHAVIORAL SCIENCE

## Epidemiology

Click on the
-?-
to reveal/hide the answer.

Prevalence is the total number of cases in a population at a
given time; -?-incidence
is the number of new cases in a population per unit time.

Sensitivity is the number of true positives divided by the
number of -?-all
people with the disease.

-?-specificity
is the number of true negatives divided by the number of all people without the
disease.

Positive
-?-predictive
value (PPV) is the number of true positives divided by the number of people who
tested positive for the disease.

The higher the prevalence of a disease, the
-?-higher
the positive predictive value of the test.

Odds ratio (OR) approximates the relative risk if the
prevalence of the disease is not too high; it is used for case-control (-?-retrospective)
studies.

Relative risk is used for
-?-cohort
studies.

SEM
-?-decreases
as n increases.

A positive skew is asymmetric with an elongated tail on the
-?-right
side of a distribution.

-?-validity
is whether a test truly measures what it purports to measure and its
-?-reliability
describes its dependability.

-?-ANOVA
checks difference between the meaning of 3 or more groups; and
-?-t-test
checks the difference between the means of two groups; and,
-?-χ

^{2}checks difference between 2 or more percentages or proportions of categorical outcomes (not mean values).A cohort study has its sample chosen based on the presence or
absence of -?-risk
factors and its subjects are followed over time for development of disease.

Clinical trials which compares the therapeutic benefit of 2
or more treatments is the highest-quality study when
-?-randomized
and double-blind.

-?-Sampling bias | subjects are not representative |

-?-Late-look bias | information gathered at an inappropriate time |

-?-Selection bias | subjects choose group |

-?-Recall bias | knowledge of presence of disorder alters recall by subjects |

Selection bias Recall bias Sampling bias Late-look bias |

A -?-type I error (α) states that there IS an
effect or difference when none exists (mistakenly accepts experimental
hypothesis and rejects the null hypothesis) and a -?-type II error
(β) states that there is IS NOT an effect or difference when one exists.