Statistics

Question 1

What is the basic idea behind using statistics in health and fitness assessments?

Answer 1

Statistics help us make sense of data and find patterns, allowing us to predict things like someone’s fitness level or health risks.

Question 2

Why is statistics important?

Answer 2

Statistics are important because they have predictive utility and allow us to understand and interpret data

Question 3

How can statistics be used to make predictions?

Answer 3

Statistics can reveal linear relationships that allow us to predict values, such as VO2 max from heart rate using the Fick equation.

Question 4

What does ‘variability’ mean when we’re talking about data?

Answer 4

Variability refers to how spread out the data is. It tells us if the values are tightly clustered or scattered widely

Question 5

What is the ‘mean’ and how is it calculated?

Answer 5

The mean is the average of a set of numbers. You find it by adding all the numbers together and then dividing by how many numbers there are

Question 6

What does ‘standard deviation’ (SD) tell us about a group of data?

Answer 6

The standard deviation measures the variability within a specific group of people being assessed. A higher SD means the data is more spread out

Question 7

If you have a standard deviation, what percentage of the data will be within 2 SDs?

Answer 7

Approximately 95% of the data will fall within a range of two standard deviations from the mean

Question 8

What is ‘standard error’ (SE) and how is it different from SD?

Answer 8

Standard error measures the accuracy of the true mean for a population, whereas SD is the variability within the sample. SE is used when we want to generalize results to a larger group

Question 9

How is standard error affected by sample size?

Answer 9

The standard error depends on both the standard deviation and the sample size. As the sample size gets bigger, the standard error decreases

Question 10

What is the Coefficient of Variation (CV) and why do we use it?

Answer 10

The CV, also known as relative standard deviation, standardizes the SD, allowing for comparison of variability when means are different. It accounts for the fact that SD depends on the mean.

Question 11

How do you calculate the Coefficient of Variation?

Answer 11

CV = (Standard Deviation / Mean) * 100

Question 12

What are some factors that can influence variability in data?

Answer 12

Variability can come from biological factors (like mood), technical issues (precision of tools), testing methods, and even the environment.

Question 13

What’s the difference between ‘validity’ and ‘reliability’?

Answer 13

• Validity is about accuracy or correctness. It asks, “does the test measure what it should?”
• Reliability is about precision and repeatability. It asks, “can the test be trusted to give the same results?”

Question 14

Why is validity important in fitness testing?

Answer 14

Validity is important because it ensures that the tests we use are actually measuring what we intend them to measure

Question 15

What are the main types of validity?

Answer 15

The main types of validity are content-related, criterion-related, face, concurrent, construct, and predictive validity

Question 16

What is ‘face validity’?

Answer 16

Face validity is about whether a test appears to measure what it’s supposed to measure.
* For example, a balance test looks like it measures balance, but is the weakest form of validity and does not have statistical verification

Question 17

What is ‘construct validity’?

Answer 17

Construct validity assesses whether a test captures the information about the underlying concept it’s trying to measure.

• An example is cardiorespiratory fitness assessed by peak aerobic power

Question 18

What is ‘concurrent validity’?

Answer 18

Concurrent validity checks if a test gives similar results to a test that has already been proven to be valid.

• An example would be comparing the results of a 2000m rowing race on a machine to an actual on-water race

Question 19

What is ‘predictive validity’?

Answer 19

Predictive validity assesses whether a test can predict a future outcome or another variable of interest

ex: using skinfold measurements to predict body fat.

Question 20

Why is reliability important in testing?

Answer 20

Reliability is important because it indicates how consistent the test results are. We need to know if we can trust a measurement to be repeatable

Question 21

What is ‘systematic error’?

Answer 21

Systematic error is a consistent type of error that causes measurements to change in one direction on repeated tests, such as from learning or fatigue

Question 22

What is ‘random error’?

Answer 22

Random error is variability that can increase or decrease test scores unpredictably on repeated tests.

Question 23

What is ‘inter-rater reliability’?

Answer 23

Inter-rater reliability compares measurements taken by two or more different testers

Question 24

What is ‘intra-rater reliability’?

Answer 24

Intra-rater reliability compares measurements taken by the same tester at different times

intra-rater reliability is also known as test-retest relability

Question 25

What is 'test-retest reliability'?

Answer 25

Test-retest reliability measures how consistent the results are when the same test is done on two or more occasions. It measures the reliability of a technique | test-restest is also known as intra-rater reliability

Question 26

What does 'Intra-Class Correlation' (ICC) tell us?

Answer 26

ICC measures the repeatability of a measurement

Question 27

How do you interpret ICC values?

Answer 27

ICC values are interpreted as follows: less than 0.40 is poor, 0.40 to 0.59 is fair, 0.60 to 0.74 is good, and 0.75 to 1.00 is excellent

Question 28

What does 'correlation' measure?

Answer 28

Correlation measures the strength of the relationship between two variables, but does not mean one causes the other

Question 29

Does correlation mean causation?

Answer 29

No, correlation does NOT mean causation.

Question 30

What is the difference between correlation and regression?

Answer 30

Correlation describes the strength of a relationship between two variables, while regression describes the numerical relationship and the pattern.

Question 31

What is 'regression' used for?

Answer 31

Regression is used to describe the numerical relationship between variables and can show the pattern of that relationship using a line of best fit

Question 32

Can relationships be non-linear in regression?

Answer 32

Yes, regression models can be non-linear to fit more complex patterns

Question 33

Can multiple factors influence the relationship between variables?

Answer 33

Yes, multiple factors can influence the relationship between variables

Question 34

What is 'Bland-Altman' analysis used for?

Answer 34

Bland-Altman analysis is used to compare a new measurement technique to a gold standard, showing how well they agree.

Question 35

Why is Bland-Altman analysis important?

Answer 35

It helps determine if a new test is accurate enough to replace a more complex or expensive “gold standard” test

Question 36

What is the purpose of Risk and Odds Ratios?

Answer 36

Risk Ratios (RR) and Odds Ratios (OR) are used to represent the effect of an intervention on a particular outcome, often used in medicine to normalize results compared to a control group

Question 37

What is an 'odds ratio' (OR)?

Answer 37

An odds ratio tells us the odds of an event happening with a particular exposure compared to a control

Question 38

How is an odds ratio calculated?

Answer 38

OR = (a/b)/(c/d) = (a*d) / (b*c) * where 'a' and 'c' are the exposed and unexposed groups, respectively, who had the event and 'b' and 'd' are the exposed and unexposed groups, respectively, who did not have the event.

Question 39

What does an odds ratio of 1 mean?

Answer 39

An OR of 1 indicates that the odds of the event are the same in the exposed and unexposed groups

Question 40

What is a 'risk ratio' (RR)?

Answer 40

A risk ratio identifies the probability of an outcome with a specific exposure or intervention

Question 41

How is a risk ratio calculated?

Answer 41

RR = [a/(a+b)] / [c/(c+d)] * where 'a' and 'c' are the number in the exposed and unexposed groups, respectively, who had the event and 'b' and 'd' are the number in the exposed and unexposed groups, respectively, who did not have the event. This number can be multiplied by 100 to get a percentage

Question 42

What are meta-analyses used for?

Answer 42

Meta-analyses combine data from multiple studies to create a larger sample size and to increase the statistical power to interpret variability and determine validity

Question 43

Why are meta-analyses important?

Answer 43

Meta-analyses are highly regarded for interpreting variability and controversy in data and they are the basis for many clinical practice guidelines

Question 44

How do meta-analyses contribute to evidence-based practice?

Answer 44

Meta-analyses help determine the validity of outcomes across different studies, leading to more reliable clinical guidelines

Question 45

What is the key concept in "Representing Data"?

Answer 45

The key concept is understanding how data is presented and interpreted using tools like mean, standard deviation, and standard error

Question 46

How does understanding these statistics help in health and fitness?

Answer 46

By knowing about variability, validity, reliability, and risk assessment, we can make better decisions, design better tests, and interpret data more effectively

Question 47

What are the two content-related validity?

Answer 47

Face and Construct validity

Question 48

What are the two criterion-related validity?

Answer 48

Concurrent and predictice validity