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DPT 673 - Research Statistics > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

Intro to Biostatistics

Statistical characteristic of population is a ?

A

Statistical characteristic of population is a parameter.

  • Population = The entire set of people in the group of interest
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2
Q

Intro to Biostatistics

Statistical characteristic of sample is a ?

A

Statistical characteristic of sample is a statistic.

  • Sample = Subset of the population chosen for study.
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3
Q

Intro to Biostatistics

The “spread” of the data = ?

A

Variability

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4
Q

Intro to Biostatistics

Measures of Central Tendency?

A
  • Mean: average
  • Median: the score at which 50% of the scores are above and below
  • Divides scores in two equal halves
  • Mode: the score that occurs most frequently

Median is between mean and mode in skewed distributions.

Central Tendency = the statistical measure that identifies a single value as representative of an entire distribution.”

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5
Q

Intro to Biostatistics

Shapes of distributions include?

A

Normal (B):

Skewed to right (A):
* The “tail” faces right; not where the bulk of the curve lies
* AKA “positive skew”
* Mean > median/mode

Skewed to left (C):
* The “tail” faces left
* AKA “negative skew”
* Mean < median/mode

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6
Q

Intro to Biostatistics

The Normal Distribution

A

The Normal Distribution

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7
Q

Intro to Biostatistics

68% of the scores are within +/- _ ? _ SD of the mean.

The Normal Distribution

A
  • 68% of the scores are within +/- 1 SD of the mean.
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8
Q

Intro to Biostatistics

95% of the scores are within +/- _ ? _ SD of the mean.

The Normal Distribution

A

95% of the scores are within +/- 2 SD of the mean.

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9
Q

Intro to Biostatistics

99% of the scores are within +/- _ ? _ SD of the mean.

The Normal Distribution

A

99% of the scores are within +/- 3 SD of the mean.

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10
Q

Intro to Biostatistics

A z-score of “2” is interpreted as?

A

A z-score of “2” is interpreted as 2 standard deviations from the mean

  • Z-Score: A standardized score based on the normal distribution
  • z = standard deviation units
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11
Q

Foundations of Statistical Inference

The likelihood that any one event will occur, given all the possible outcomes = ?

A

Probability = The likelihood that any one event will occur, given all the possible outcomes.

  • Represented by a lowercase p
  • Implies uncertainty – what is likely to happen
  • Essential to understand inferential statistics
  • Many statistical tests assume data are normally distributed
  • Relationship to normal distribution
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12
Q

Foundations of Statistical Inference

Sampling error measured by ?

A

Sampling error measured by the standard error of the mean.

  • The sample mean won’t equal the population mean = Difference is called sampling error.
  • If you repeat the study using new samples from the SAME population, how much with the sample mean vary?
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13
Q

Foundations of Statistical Inference

A range of values that we are confident contains the population parameter = ?

A
  • Confidence Interval = A range of values that we are confident contains the population parameter.
  • Width concerns the precision of the estimate

95% Confidence Interval =
* If we repeated sampling an infinite number of times, 95% of the intervals would overlap the true mean

  • The 95% CI of 5 from 100 samples will not overlap the true population mean
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14
Q

Foundations of Statistical Inference

Reject Ho + Ho is true = __?__

Potential Errors in Hypothesis Testing

A

Type 1 error / Liar

False positive / Dr. says “You’re pregnant” + you’re male

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15
Q

Foundations of Statistical Inference

Reject Ho + Ho is false = ?

Potential Errors in Hypothesis Testing

A

Correct

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16
Q

Foundations of Statistical Inference

Accept “do not reject “ Ho + Ho is true = ?

Potential Errors in Hypothesis Testing

A

Correct

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17
Q

Foundations of Statistical Inference

Accept “do not reject “ Ho + Ho is False = __?__

Potential Errors in Hypothesis Testing

A

Type 2 Error / Blind

False negative, You’re pregnant + Dr. says “You’re not pregnant”

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18
Q

Foundations of Statistical Inference

Alpha = ?

A
  • Maximum probability of type 1 error
  • Set by researcher before running statistics
  • Usually set to 0.05 (max chance of type 1 error = 5%)
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19
Q

Foundations of Statistical Inference

P-value = ?

A

Formal definition:

  • P-value = probability of observing a value more extreme than actual value observed, if the null hypothesis is true.

Simple definition:

  • P-value = Probability of Type 1 error, if the null hypothesis is true.
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20
Q

Foundations of Statistical Inference

If P-value < alpha = ?

Decision Rule

A
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21
Q

Foundations of Statistical Inference

If P-value > alpha = ?

Decision Rule

A
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22
Q

Foundations of Statistical Inference

If we “fail to reject” (accept) Ho, we attribute any observed difference to ?

A

If we “fail to reject” (accept) Ho, we attribute any observed difference to sampling error only.

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23
Q

Foundations of Statistical Inference

If 95% CI of “mean difference” includes zero = ?

A

Non-significant because includes 0.

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24
Q

Foundations of Statistical Inference

What should you know about one vs. two-tailed tests?

A
  • One-tailed test for directional hypothesis
  • Two-tailed test for nondirectional hypothesis
  • Two-tailed test allows for possibility that difference may be positive or negative.
  • One-tailed test more powerful
  • More power = more likely to find significance when there is significance.
  • Less likely to commit Type II error
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25
Q

Foundations of Statistical Inference

The probability of finding a statistically significant difference if such a difference exists in the real world = ?

A

Statistical Power = The probability of finding a statistically significant difference if such a difference exists in the real world

  • The probability that the test correctly rejects the null hypothesis
  • Only matters when the null is false
26
Q

Foundations of Statistical Inference

The Four Pillars of Power?

27
Q

Foundations of Statistical Inference

How to manipulate the four pillars to increase power?

28
Q

Foundations of Statistical Inference

How to manipulate the four pillars to decrease power?

29
Q

Foundations of Statistical Inference

Determinants of Statistical Power?

A

P = power (1 – β)
A = alpha level of significance
N = sample size
E = effect size

  • Knowing three of these four will allow for determination of the fourth.
30
Q

Foundations of Statistical Inference

A priori = ?

Power Analysis

A

A priori = before data collection

  • Minimum sample required
31
Q

Foundations of Statistical Inference

Post hoc = ?

Power Analysis

A

Post hoc = after data collection

  • Only an issue if you fail to reject the null hypothesis
32
Q

Foundations of Statistical Inference

Power with MCID and CI.

33
Q

Type I and Type II Errors

A physical therapist conducts a study on the relationship between age and flexibility of the hamstrings.

  • What is the null hypothesis?
  • Write a directional alternative hypothesis for this scenario.
A

A physical therapist conducts a study on the relationship between age and flexibility of the hamstrings.

  • What is the null hypothesis? = There is no correlation between age and hamstring flexibility.
  • Write a directional alternative hypothesis for this scenario. = There is a significant negative correlation between age and hamstring flexibility.
34
Q

Type I and Type II Errors

The physical therapist gathers data, and the data suggest there is no correlation between age and hamstring flexibility, when there truly is a negative correlation.

  • Is the researcher correct or is this an error? If an error, what type?
  • If this is an error, what could the researcher do to mitigate this error?
A

The physical therapist gathers data, and the data suggest there is no correlation between age and hamstring flexibility, when there truly is a negative correlation.

  • Is the researcher correct or is this an error? If an error, what type? = This is a Type II error. “Failure to reject a FALSE null.”
  • If this is an error, what could the researcher do to mitigate this error? = This may be an issue of power. Two options are to increase the sample size or to increase alpha. Increasing alpha will decrease beta, which will increase power (1 – B). Increasing sample size is the better option.
35
Q

Type I and Type II Errors

A physical therapist conducts a study on the relationship between age and flexibility of the hamstrings.

  • What is the null hypothesis? =
  • Write a nondirectional alternative hypothesis? =
A

A physical therapist conducts a study on the relationship between age and flexibility of the hamstrings.

  • What is the null hypothesis? = There is no correlation between age and hamstring flexibility.
  • Write a nondirectional alternative hypothesis? = There is a significant correlation between age and hamstring flexibility.
36
Q

Type I and Type II Errors

The physical therapist gathers data, and the data suggest there is a negative correlation between age and hamstring flexibility, when there truly is a negative correlation.

  • Is the researcher correct or is this an error? If an error, what type?
  • If this is an error, what could the researcher do to mitigate this error?
A

The physical therapist gathers data, and the data suggest there is a negative correlation between age and hamstring flexibility, when there truly is a negative correlation.

  • Is the researcher correct or is this an error? If an error, what type? = **The researcher is correct. **
  • If this is an error, what could the researcher do to mitigate this error? = N/A
37
Q

Review of Experimental Designs

True experimental or Quasi-experimental design?

A

True experimental design.

38
Q

Review of Experimental Designs

What Design?

A

Pretest-Posttest Control Group Design:
* Both groups are measured before and after treatment
* Differences between the groups can be attributed to the treatment
* Cause and effect (AKA, causation, causal relationship)

39
Q

Review of Experimental Designs

A

Designs for Repeated Measures:
* Same people in each level of the IV = “within-subject design”
* Single factor (one-way) repeated measures design
* There is no control group – subjects act as their own controls

40
Q

Review of Experimental Designs

Time can be the IV in a ?

A

Time can be the IV in a single-factor repeated measures design.

41
Q

Comparing Two Means

Assumptions of Parametric Tests = __?__

A
  1. Scale data (ratio or interval) - Calculate means and variance, so data should be continuous
  2. Random Sampling - Though this is rare in PT research
  3. Equal Variance- Used when there is more than one group. T-test, ANOVA. Groups were “roughly equivalent” before starting. Can be tested statistically
  4. Normality - Data are sampled from a population with a normal distribution. Can be tested statistically
42
Q

Comparing Two Means

Independent groups or Repeated measures?

43
Q

Comparing Two Means

Independent groups or Repeated measures?

44
Q

Comparing Two Means

If t > 1, you have = ?

If t < 1, you have = ?

A
  • If t > 1, you have a greater difference between groups
  • If t < 1, you have more variability within groups
45
Q

Comparing Two Means

The number of independent pieces of information that went into calculating the estimate= __?__

A

Degrees of freedom = The number of independent pieces of information that went into calculating the estimate.

46
Q

Comparing Two Means

What kind of T-Test for independent groups?

47
Q

Comparing Two Means

Independent (unpaired) t-test protocol?

48
Q

Comparing Two Means

Assumptions for Unpaired t-Tests?

49
Q

Comparing Two Means

Cohen’s d = ?

50
Q

Comparing Two Means

What kind of T-Test for repeated measures?

51
Q

Comparing Two Means

Paired t-test protocol?

52
Q

Comparing Two Means

Assumptions for Paired t-Tests

53
Q

t-Test Concepts

A test for equal variances for independent groups t-test (and ANOVA) = __?__

A

Levene’s Test: A test for equal variances for independent groups t-test (and ANOVA)

Tests the null hypothesis:
* There is no significant difference in variance between groups the same p-value rules apply.

  • p < .05 we REJECT the null hypothesis = i.e. variances are NOT equal
  • p > .05 we ACCEPT (fail to reject) the null hypothesis
    i.e. variances ARE equal
54
Q

t-Test Concepts

Conceptual basis of comparing means: independent groups?

55
Q

t-Test Concepts

Conceptual basis of comparing means: repeated measures?

56
Q

ANOVA Concepts

Basics of analysis of variance (ANOVA)?

57
Q

ANOVA Concepts

Types of ANOVAs

58
Q

ANOVA Concepts

Power and effect size (for ANOVA).

59
Q

ANOVA Concepts

Multiple comparison tests for independent groups?

60
Q

ANOVA Concepts

Multiple comparison tests for repeated measures?

DPT 673 - Research Statistics (3 decks)

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