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Statistics Of Physical Activity > Final Exam Term Review > Flashcards

Final Exam Term Review Flashcards

1
Q

Measurement

A

the process of comparing a value to a standard
- Distance
- Time
- Force
- Frequency

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2
Q

Reliability

A

Reproducibility and consistency

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3
Q

Validity

A

The soundness or appropriateness of a test in measuring what it’s supposed to

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4
Q

Variable

A

Characteristics of a person, place or thing that can assume more than one value

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5
Q

Constant

A

A characteristic that does not change

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6
Q

Nominal measurement**

A

Grouping participants/objects into categories

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7
Q

An example of Nominal measurement**

A
  • Young to old
  • Undergraduate Program
  • Country of origin
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8
Q

Ordinal measurement

A

Rank participants/objects

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9
Q

An example of Ordinal Measurement**

A

Ranking in sports (1st place - 8th place)

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10
Q

Interval measurement**

A

Equal unit of measurement with no true zero

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11
Q

An example of interval measurement**

A

Temperature (0 degrees = cold)

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12
Q

Ratio measurement**

A

Scale that has an absolute zero

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13
Q

An example of Ratio measurement**

A
  • Distance
  • Weight
  • Mark on an exam
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14
Q

What is interval validity?

A

A measure of control within the experiment to ascertain that the results are due to the treatment that was applied

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15
Q

What is external validity?

A

The ability to generalize the results of the experiment to the population from which the samples were drawn

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16
Q

What is a Normal Curve (Gaussian curve)?

A
  • Symmetrical, bell shaped curve
  • Measures how frequently scores appear, most frequent towards the middle
  • Frequency declines farther from the centre of the graph
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17
Q

What is the Central limit Theorem?

A

A sum of random numbers becomes normally distributed as more and more random numbers are added together

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18
Q

Types of curves

A
  1. bimodal
  2. Negatively or Positively skewed curve
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19
Q

What is Grouped Frequency Distribution?

A
  • Grouped data into bins (a range of values)
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20
Q

When should grouped frequency distribution be used?

A

If number of samples is greater than 20 and range is greater than 20
(form 15 groups)

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21
Q

What is a percentile?

A

A point or position on a continuous scale of 100 such that a certain fraction of the population of raw scores lies at or below that point

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22
Q

Why are percentiles useful?

A

They use standard scroes that
- evaluate raw scores
- compare two sets of scores that have different units

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23
Q

Mode disadvantages

A
  • unstable and may change
  • not useful for further calculations
  • ignores extreme scores
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24
Q

Mean

A
  • affected by every other score and outliers can greatly affect it
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25
Q

Relationship of mean, median and mode when data is normally distributed

A

All three values will fall near or at the same value.

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26
Q

When do you use mean?

A
  • Data is near normal
  • Interval or ratio type of measurement
  • further calculations are needed (SD)
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27
Q

When to use median

A
  • data is ordinal
  • middle score is needed
  • most typical score needed
  • curve is badly skewed
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28
Q

Use mode when

A
  • only a rough estimate is needed and data is nearly normal
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29
Q

What is variability?

A

The scatter of scores in a distribution

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30
Q

What is deviance? (d)

A

Distance of each raw score from the mean

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31
Q

Total sum of deviance

A

Should equal 0

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32
Q

What is variance? (V or s2)

A

Average distance between the mean and the data points

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33
Q

What is standard deviation?

A

The square root of the variance (sensitive to extreme scores)

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34
Q

What is standard deviation used for?

A

to summarize variability in a data set (easiest to interpret)

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35
Q

What does the standard deviation reflect?

A

Reflects the deviation from the mean
- most common measure of variability

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36
Q

What is the coefficient of variation?

A

Comparison of two standard deviations

37
Q

Standard deviation an dthe normal distribution

A

The larger N is the more close to normal the distribution is
- Usually five to six SD in a range in a data set
- Does not apply to skewed data or small N data

38
Q

What is a parameter?

A

Value that describes a population
- derived from measurements of all individuals in the population

39
Q

What is a statistic?

A

Value that describes a sample
- derived from measurements of individuals in the sample

40
Q

How to use sample data?

A
  1. Estimate the usual response
  2. Need to know how much error in our estimation
41
Q

What is the best estimate when using sample data

A

Mean +/- Standard Deviation

42
Q

How do we estimate error in a sample?

A

Standard error of the mean (SEM)

43
Q

What is standard error of the mean? (SEM)

A

The amount of error that may exist when a random sample eman is used to predict a population mean

44
Q

Two ways to reduce SEM

A
  1. Increase sample size
  2. Decrease Standard deviation
45
Q

What do Z scores allow us to do?

A
  1. Compare to the normal curve
  2. Make predictions
  3. Test hypotheses
  4. Understand the risk of being wrong
46
Q

Steps of running an experiment

A
  1. Form a hypothesis
  2. Collect data in a valid and reliable fashion
  3. Find out if our hypothesis is correct
  4. Understand how likely we are t be wrong in our conclusion
47
Q

How to form a statistical hypothesis

A

Create two mutually exclusive (only one can be true) and exhaustive (no other option can exist) mathematical statements about the outcome of analysis

48
Q

Null statistical hypothesis

A

No different between 2 groups
X - X = 0

49
Q

Alternate statistical hypothesis

A

There is a difference between the 2 groups
X - X does not = 0

50
Q

What is the decision of statistical significance?

A

This decision is based on the probability that you might be wrong
It’s the degree of risk your willing to take that you will reject the null hypothesis when it’s actually true

51
Q

What is significance level?

A

Risk associated with nit being 100% positive that what occurred in the experiment is a result of what you did or what is being tested

52
Q

What is the most commonly used level of confidence?

A

p < .05
5% chance that you reject the null hypothesis when it’s true
95% confident in your conclusion

53
Q

How is the level of confidence set?

A

Depending on how strict a situation is
Vaccine = low p
Trying a new diet = high p
You want a lower probability of being wrong when something is life threatening to the population

54
Q

Correlation Coefficient

A

Measures the extent to which 2 variables are related
- tells us magnitude and direction of linear relationship

55
Q

Why are correlations useful?

A

Allows us to predict values for 1 variable based on a known value of the other

56
Q

What is a line of best fit?

A

The average between all data points

57
Q

Where are error points found?

A

Around the line of best fit

58
Q

Where is the line of best fit with no correlation?

A

There is no line of best fit = no relationship between two variables (grades and long jump distance)

59
Q

What is a correlation? Pearsons r

A

the extent to which direction and size of deviations from the mean in one variable are related to the direction and size of deviations from the mean in another variable

60
Q

Positive corrleation

A

Subjects who score above mean on X score above mean on Y score and vice versa

61
Q

Negative correlation

A

Subjects who score below mean on X score above mean on Y score and vice versa

62
Q

What range does Pearson r values range from ?

A

+1.00 to -1.00
Closer r is to these values, the stronger the relationship

63
Q

What if r is 0.0?

A

There is no relationship between the variables

64
Q

What is the coefficient of determination?

A

The percentage of 1 variable that is accounted fr by variance in the other variable
The stronger the correlation, the more variance that can be explained

65
Q

What is bivariate regression?

A

A way to make predictions using scores on 1 variable to predict scores on another.

66
Q

What can bivariate regression estimate?

A

Future outcomes from present ones

67
Q

What does the regression equation describe?

A

Describes the line of best fit (regression line), then we can make infinite predictions

68
Q

error of prediction

A

distance of the actual measure from the line of best fit (prediction)
= sum of Y - Y’ = 0

69
Q

What does every data point have?

A

An error in prediction called the residual (Y’)

70
Q

What is the residual? (Y’)

A

The vertical distance between actual data and the line of best fit = best prediction of Y at any x value

71
Q

What is multiple regression?

A

Multiple predictions and a single dependent variable

72
Q

Why should we use multiple regressions?

A
  1. more than one independent variables will better predict the dependent variable
  2. Total variance explained by more than one independent variables
  3. Leads to greater r squared
73
Q

R for multiple regression

A

Can’t be negative and R = 0 means there is no relationship between the independent and dependent variables
R = 1.0 means perfect relationship

74
Q

Multiple correlation coefficient squared

A

R2 = 0.72 = 72% variance in dependent variable (Y)

75
Q

What is a t-test?

A

Compare the means of 2 different groups of samples (or 2 levels of an independent variable)

76
Q

What can independent variable be?

A
  • groups of people
  • task conditions
  • time points
77
Q

What are repeated measures?

A

Tests the same subjects twice to reduce standard error and increases degrees of freedom

78
Q

What does repeated measures t-test compare?

A

2 means from the same group in a pre-post design and must have all participants for both conditions

79
Q

Advantages of independent designs between groups

A
  • Can be less time-consuming
  • Participants only complete 1 experimental condition
  • Often a one-time collection
80
Q

Advantages of repeated measures design within groups

A
  • Accounts for individual differences
  • Greater power to detect a difference
81
Q

When is an ANOVA used? (analysis of variance)

A

Used when more than two group means are being tested simultaneously
- participants are only tested once and only one dependent variable
- 2+ independent variables

82
Q

Why can’t we use multiple t-tests?

A

Because t-tests on the same data increases type 1 error rate and hey only consider variance for 2 samples, ignoring variance for the 3rf.

83
Q

What is required with an ANOVA?

A

A post hoc test that identifies which group is different from the others

84
Q

What are post hoc tests?

A

Comparison between pairs of treatment levels
Run when there is a significant difference in ANOVA

85
Q

Tukey’s (HSD)

A

Allows only pairwise comparisons and simplier to calculate while applying most research designs

86
Q

What is the main effect of a factorial ANOVA?

A

When an independent variable has a significant effect upon the dependent variable

87
Q

What is the interaction effect?

A

The effect on the dependent variable depends on >1 independent variable

88
Q

Test of association

A

Equivalent to Pearson’s r correlation test
except data can be curvilinear and not normally distributed

Statistics Of Physical Activity (9 decks)

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