Round 2 Lecture 3 Flashcards Preview

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Flashcards in Round 2 Lecture 3 Deck (17)
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1

A Parameter

is a summary measure computed to describe a characteristic of the population

2

A Statistic

is a summary measure computed to describe a characteristic of the sample

3

A Hypothesis

is a claim about a population parameter

4

Null Hypothesis (H0)

 Opposite of the alternative hypothesis
 May or may not be rejected based on empirical evidence

5

Alternative Hypothesis (Ha)

Generally the hypothesis that the researcher claims

6

two-sided alternative

we are equally interested in deviations on either side of the null hypothesis value.

7

one-sided alternative

An alternative hypothesis that focuses on deviations from the null hypothesis value in only one direction

8

Logic of Hypothesis Test

-We start by assuming that the null hypothesis is true
-Then we look at the data and check if there is enough evidence to reject the null hypothesis.
-This is the logic of jury trials. In court, the null hypothesis is that the defendant is innocent.
-The evidence takes the form of facts that seem to contradict the presumption of innocence.

9

P-value

is the probability of seeing the observed data (or something even less likely) given the null hypothesis.

10

statistically significant

We can define a “rare event” arbitrarily by setting a threshold for our P-value. If our P-value falls below that point, we’ll reject the null hypothesis

11

The threshold is called

an alpha level

12

The alpha level is also called

the significance level

13

False positive

The null hypothesis is true, but we mistakenly reject it.
type 1 error

14

False Negative

The null hypothesis is false, but we fail to reject it.
type 2 error

15

Hypothesis about dependence between two discrete (categorical) variables:

Use Chi-square test

16

 Hypothesis about means of two different groups (i.e., relationship between a continuous variable and a discrete variable with two categories):

Use t-test

17

 Hypothesis about means of multiple (>two) different groups:

Use One-way ANOVA test