Modules 19-20 Learning outcomes

Question 1

L19 - Sampling: Symbols

Population

Sample parameters

• KNOW SAMPLE SIZE CALC

Answer 1

Population
* Mean = μ
* Standard deviation = σ

Sample parameters
* Mean (x̄),
* Standard deviation= (s)

Question 2

L19 - Sampling:
- Purpose

Answer 2

Access information on the population of interest in a reasonable / do-able fashion

• Help eliminate selection bias
• Account for natural variable in ethnicity, sex, education, etc. that exist in the population of interest

Question 3

L19 - Sampling: Bias

inclusion and exclusion in a study

Answer 3

• Occurs when the individuals selected for a sample over- represent or under-represent certain population attributes
  that are related to the phenomenon
• Random grouping minimizes bias
• refers to the primary traits of
  the target and accessible populations that will qualify someone as a subject
• refers to those factors that
  would exclude someone from being a subject

Question 4

L19 - Sampling:
- Random Sampling (Probability Sampling)

Answer 4

• These methods give every member of
  the population a known and often equal chance of being selected
• Random selection enhances external validity

1. Simple Random Sampling
   * Use of a random numbers table
2. Systematic Sampling
   * Another method of sampling from the population with a defined
   sample size number. Pick every blank one
3. 3. Stratified Random Sampling
      * Population is too large
      * Divide the population into “strata”
4. Cluster sampling. Cluster based on size

Question 5

L19 - Sampling:
- Non-Random Sampling (Non-Probability Sampling)

Answer 5

• Non-random sampling can introduce
  sampling bias, which occurs when the sample is not representative of the
  population
• Convenience sampling: Chosen on basis of availability
• Snowball (similar to convince sampling but more selective). deemed fitting (certain trait)
  -Purposive (sampling with a defined purpose)
• Expert sampling

Question 6

L19 - Sampling:

Random selection vs random assignment

Answer 6

Random Selection (external validity)
* Random selection from the population of interest

• Random Assignment (internal validity)
• If you are comparing groups, then random assignment to groups

Question 7

L20 - Variability-bias-confounding:

-Frequency Distributions

Statistical tools mostly rely on normal distribution

Answer 7

• frequency distribution is a table that displays the frequency of
  various outcomes in a sample

Your data is assumed to be normally distributed even if your sample does not look like it.
T-test only work on normal distributions ! With continuous values

Question 8

L20 - Variability-bias-confounding:

Bias vs confounding variability

Answer 8

Bias: bias refers to any systematic error in the design, conduct, or analysis of a study that can lead to a distortion of the true effect or association

• Confounding Variability: These are other variables that are related to both the independent and dependent variables and can influence the observed relationship. Controlling for confounding variables is crucial for establishing causality in experimental designs.

Question 9

Types of variables

Nominal Variables:

Ordinal Variables:

Answer 9

Nominal Variables: These are categorical variables where the categories have no inherent order (e.g., gender, types of exercise)

Ordinal Variables: Categorical variables where the categories have a meaningful order or ranking, but the intervals between categories
are not necessarily equal (e.g., levels of pain: mild, moderate, severe)

Question 10

types of variables

Continuous Variables:

Ratio variables:

Answer 10

Continuous Variables: These are variables that can take on any value
within a given range and have equal intervals between values (e.g., height,
weight, time)

Ratio variables: numeric measurements with equal intervals between values and an absolute zero point (e.g., VO₂max scores, heart rates)