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Flashcards in Inferential Statistics Deck (22)
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1

To make a decision about the population group based on the information of the sample group

Inferential Statistics

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Population

The complete collection to be studied

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Sample

part of the population of interested selected for the study

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Sampling errors

differences between the sample values and population values

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Sample selection is required to be __

random

random selection implies all members have an equal opportunity to be chosen

doesn't guarantee proportional representation of all parts of the population

6

Sampling distribution of a statistic

distribution of values taken by the statistic in all possible samples of the same size from the same population

7

Sampling distribution of means

distribution of sample means. Called standard error of the mean.

8

Sampling distribution of means formula

𝑠/√𝑛
where n = sample size

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Common use of Inferential Statistics

estimate population parameters

compare between groups

10

SHT procedure

1. state statistical hypotheses (null and alternative)

2. Select level of significance

3. Decide which test to use

4. Make a decision to reject or retain null hypothesis based on p value

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p value

major resulting value of running a SHT

quantifies how consistent your sample values are with null hypothesis

ranges from 0-1

12

large p value

closer to 1:
sample values are consistent with null hypothesis

13

small p-value

closer to 0:
sample values are not consistent with null hypothesis

14

how to make decision to reject or retain null hypothesis based on p value and 𝛼

compare p value to preset level of significant (𝛼).

if 𝛼= 0.05 and p value < 0.05, reject null hypothesis

if p value > 0.05 retain null hypothesis.

15

if p > .05 you can decide that

observed difference is not significant

16

if p < .05 you can decide that

observed difference is significant

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Potential errors for SHT

any one decision can be correct or incorrect

it can be classified into four possible decision outcomes

18

if the null hypothesis is true and you reject the null hypothesis, this is considered

Type I error

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if the null hypothesis is false and you reject the null hypothesis this is

correct

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if the null hypothesis is false and you do not reject the null hypothesis this is

Type II error

21

If the CI contains a null value:

the effect is not significant

22

If the CI does not contain the null value

effect is significant