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Question 1

What does sampling seek to achieve in research?

Answer 1

To generalize from sample to population.

Question 2

What is the population mean represented by in statistics?

Answer 2

𝜇

Question 3

What is the sample mean represented by in statistics?

Answer 3

𝑋

Question 4

What is estimation in the context of statistics?

Answer 4

Estimating the population mean (𝜇) using the sample mean.

Question 5

What is the purpose of a confidence interval?

Answer 5

To estimate the range within which the true population mean (𝜇) will fall.

Question 6

What levels of confidence are commonly used in statistics?

Answer 6

• 95% * 99%

Question 7

What does hypothesis testing primarily focus on?

Answer 7

Testing hypotheses about differences between groups.

Question 8

How is the null hypothesis (Ho) defined?

Answer 8

There is no significant difference between group means.

Question 9

What is the alternative hypothesis (Ha)?

Answer 9

There is a significant difference between group means.

Question 10

What does a Type I error represent?

Answer 10

Rejecting Ho when it is actually true.

Question 11

What does a Type II error represent?

Answer 11

Failing to reject Ho when it is actually false.

Question 12

What is the level of significance in hypothesis testing?

Answer 12

Alpha (𝛼), the probability of making a Type I error.

Question 13

What is the typical value of alpha in hypothesis testing?

Answer 13

0.05 or 0.01

Question 14

What does it mean when data falls within the confidence interval?

Answer 14

Data is likely if Ho is true.

Question 15

What happens if the sample mean difference falls outside the confidence interval?

Answer 15

Data is unlikely if Ho is true, leading to rejection of Ho.

Question 16

What is the relationship between alpha and the area of rejection?

Answer 16

A larger alpha increases the area of rejection, making it easier to reject Ho.

Question 17

What is the sampling distribution of differences between means?

Answer 17

The frequency distribution of differences when Ho is assumed to be true.

Question 18

What is the purpose of setting the level of significance?

Answer 18

To determine what sample mean difference is expected or unlikely if Ho is true.

Question 19

What is the implication of a Type I error in COVID-19 testing?

Answer 19

Incorrect conclusion that one has COVID-19 when they do not.

Question 20

What is the implication of a Type II error in COVID-19 testing?

Answer 20

Incorrect conclusion that one does not have COVID-19 when they do.

Question 21

What does the null hypothesis (Ho) assume?

Answer 21

Observed differences are due to chance or sampling error alone.

Question 22

What does the alternative hypothesis (Ha) assume?

Answer 22

Observed differences are due to the variable or manipulation.

Question 23

What is the falsifiability criterion?

Answer 23

The requirement to falsify Ho to support Ha.

Question 24

How do researchers ensure they do not make a Type I error?

Answer 24

By specifying the level of significance (alpha).

Question 25

What is the expected outcome if Ho is true?

Answer 25

Most sample mean differences fall close to zero.

Question 26

What is the critical region in hypothesis testing?

Answer 26

The area where observed data is considered unlikely if Ho is true.

Question 27

What does it indicate if the sample mean difference is large?

Answer 27

It suggests a true difference between groups.

Question 28

What is the main goal of hypothesis testing?

Answer 28

To determine if there is a significant difference between groups.

Question 29

What is the statistical significance of a result?

Answer 29

The result is unlikely to be due to sampling error.

Question 30

What is a Type I error?

Answer 30

Tests concluded that the treatment has a significant effect, when in fact there is none. ## Footnote Type I error is also known as a false positive.

Question 31

What is a Type II error?

Answer 31

Tests concluded that the treatment has no significant effect, when in fact there is one. ## Footnote Type II error is also known as a false negative.

Question 32

What should you consider when determining the significance level?

Answer 32

The cost implications of Type I and Type II errors. ## Footnote Setting a lower significance level can reduce Type I error probability.

Question 33

How can you reduce the probability of a Type II error?

Answer 33

By increasing the sample size or the significance level. ## Footnote This makes it easier to reject the null hypothesis (Ho).

Question 34

What is the test statistic used for?

Answer 34

To determine if the null hypothesis can be rejected. ## Footnote It can be computed using SPSS or manually.

Question 35

What is alpha (𝛼)?

Answer 35

The probability of obtaining the minimum required sample mean difference to reject the null hypothesis (Ho). ## Footnote Alpha is expressed in terms of probability.

Question 36

What decision is made if p < 𝛼?

Answer 36

Reject the null hypothesis (Ho).

Question 37

What decision is made if p ≥ 𝛼?

Answer 37

Fail to reject the null hypothesis (Ho).

Question 38

What does statistical power indicate?

Answer 38

The probability of correctly rejecting a false null hypothesis (Ho). ## Footnote It reflects the ability to detect a real difference in the population.

Question 39

What factors increase statistical power?

Answer 39

* p level * Sample size * Effect size

Question 40

What is effect size?

Answer 40

A measure of the strength of a relationship or effect. ## Footnote It quantifies the size of the difference between two groups.

Question 41

What does a p-value indicate?

Answer 41

The probability of obtaining the difference between means by chance alone.

Question 42

How can effect size be interpreted?

Answer 42

* .10 to .30: small effect * .30 to .50: medium effect * .50 or above: large effect

Question 43

Does a small effect size always lack significance?

Answer 43

No, a small effect size can be impressive if the variable is difficult to change or very valuable.

Question 44

Why is effect size important in research results?

Answer 44

It quantifies the size of the difference and is a true measure of significance. ## Footnote Statistical significance alone does not indicate effect size.

Question 45

What should be reported when writing results?

Answer 45

* Effect size * An estimate of its likely margin of error or confidence interval (CI)