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911 Flashcards

Week 1-4

1
Q

What is the definition of statistics according to Davidian and Louis?

A

Statistics is the science of learning from data, measuring, controlling, and communicating uncertainty. It is essential for controlling the course of scientific and societal advances.

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2
Q

What are the main types of statistical paradigms?

A

Bayesian Statistics
Classical (Error) Statistics
Likelihood-Based Statistics
Akaikean-Information Criterion-Based Statistics
Frequentist Statistics

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3
Q

What defines a research question?

A

A question that defines what a study hopes to learn, addressing how, why, when, and what.

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4
Q

What are the types of research questions?

A

Descriptive: Describes what is going on or what exists.
Relational: Examines relationships between variables.
Causal: Determines if a variable affects one or more outcomes.

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5
Q

What are characteristics of a researchable question?

A

Narrow and specific.
Can be answered by observable evidence (data).
Has significant relevance for guiding policy, theory, or practice.

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6
Q

What is a hypothesis?

A

A specific, concise statement predicting the outcome of a study, indicating variables and the relationship to be examined.

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7
Q

What are the types of hypotheses?

A

Null hypothesis
Alternative hypothesis
Hypothesis of difference
Hypothesis of point-prevalence
Hypothesis of association

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8
Q

What is operationalization?

A

Defining a concept by specifying the activities or operations needed to measure it.

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9
Q

What is the difference between independent and dependent variables?

A

Independent Variables (IV): Variables that predict or cause the dependent variable.
Dependent Variables (DV): Variables that are explained or predicted.

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10
Q

What are levels of measurement in data?

A

Continuous data (Quantitative)
Categorical data (Qualitative)

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11
Q

What are the types of data in research?

A

Primary Data: Data collected by the researcher (e.g., surveys, interviews).
Secondary Data: Data previously collected for other purposes (e.g., administrative records).

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12
Q

What are the advantages and disadvantages of primary data?

A

Advantages: Collect the exact data needed, define and measure variables directly.
Disadvantages: Expensive, time-intensive, labor-intensive.

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13
Q

What are the advantages of secondary data?

A

Less expensive
Large quantities available
Depth (multiple years) and breadth (many data elements)
Easy to access (some)

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14
Q

What are some disadvantages of secondary data?

A

May not be collected for research purposes
May have data entry errors or omissions
May not measure the exact variables needed
Barriers to linking datasets (e.g., HIPAA)

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15
Q

What is the difference between Missing Completely at Random (MCAR) and Missing at Random (MAR)?

A

MCAR: Missingness has no relationship with any observed or missing data.
MAR: Missingness is related to some observed data.

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16
Q

What are the three types of statistical tests based on the purpose of analysis?

A
  1. Tests of Differences (e.g., T-test, ANOVA)
  2. Tests of Associations (e.g., Chi-square, correlation, regression)
  3. Parametric and Nonparametric tests (dependent on distribution and sample size)
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17
Q

What are the main types of descriptive statistics?

A
  1. Measures of Frequency (e.g., Count, Percent, Frequency)
  2. Measures of Central Tendency (e.g., Mean, Median, Mode)
  3. Measures of Dispersion/Variation (e.g., Range, Standard Deviation, Variance)
  4. Measures of Position (e.g., Percentile Ranks, Quartile Ranks, Z-scores)
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18
Q

What are the three measures of central tendency?

A

Mean: The arithmetic average.
Median: The middle score in a distribution.
Mode: The most frequently occurring score.

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19
Q

What is the range in descriptive statistics?

A

The range is the difference between the highest and lowest values in a dataset.

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20
Q

What is the standard deviation?

A

The square root of the average squared deviation, providing the average amount by which scores differ from the mean.

21
Q

How do you decide which measure of central tendency to use based on data type?

A

Nominal data: Use the mode.
Ordinal data: Use the median.
Interval/Ratio data (without outliers): Use the mean.
Skewed distribution (with outliers): Use the median.

22
Q

What are misleading graphs and examples of how they mislead?

A

Starting scale not at zero
Leaving scale off the graph
Selecting data that is accurate but misleading

23
Q

What is the primary goal of statistical inference?

A

To generalize results from a sample to a larger population beyond the observations in the sample.

24
Q

What is probability theory?

A

The foundation of inferential statistics, calculating the likelihood of an event occurring out of all possible outcomes.

25
Q

What are the three properties of probability distributions?

A

Probability of an event is between 0.0 and 1.0.
The total probability of all possible outcomes must add to 1.0.
Mutually exclusive events’ probabilities can be added together.

26
Q

What is the normal distribution?

A

A theoretical mathematical distribution used to evaluate the probability of events. It assumes a bell curve shape and is symmetrical, unimodal, and continuous.

27
Q

What percentage of scores fall within one, two, and three standard deviations from the mean in a normal distribution?

A

One standard deviation: 68%
Two standard deviations: 95%
Three standard deviations: 99%

28
Q

What is a Z-score?

A

A standard normal deviate representing how many standard deviation units a score falls above or below the population mean (μ).

29
Q

What is the Central Limit Theorem?

A

The theorem states that for large sample sizes (N ≥ 30), the distribution of the sample means will approach a normal distribution, regardless of the original population’s distribution.

30
Q

What is the sampling distribution of the mean?

A

A theoretical distribution of mean scores from all possible random samples of a given size from a population.

31
Q

What is the standard error of the mean?

A

The standard deviation of the sampling distribution of the mean, indicating how accurately the sample mean estimates the population mean.

32
Q

What is a confidence interval?

A

A range of score values expected to contain the population mean with a certain level of confidence (e.g., 95% or 99%).

33
Q

What is the relationship between variance and standard deviation?

A

Standard deviation is the square root of variance, providing a more interpretable measure of how much scores differ from the mean.

34
Q

What are the characteristics of a normal distribution?

A

Symmetrical
Unimodal
Continuous
Mean = median = mode
Asymptotic (tails never touch the baseline)

35
Q

What is hypothesis testing?

A

A process used to assess if results from a test are valid or repeatable, involving comparing a research hypothesis with a null hypothesis.

36
Q

What are the main steps in the hypothesis testing process?

A

Specify research hypothesis.
Specify null hypothesis.
Specify alpha (α) level.
Select sample and implement design.
Select and compute statistical test.
Compare observed significance level (p) with alpha.
Make a decision regarding the null hypothesis.

37
Q

What is a null hypothesis (H0)?

A

The hypothesis that there is no relationship between the hypothesized variables in the population.

38
Q

What is an alternative hypothesis (H1)?

A

The hypothesis that there is a relationship between the hypothesized variables in the population.

39
Q

What is a significance level (α)?

A

A probability value that provides the criterion for rejecting the null hypothesis, representing the probability of making a Type I error (rejecting the null when it is true).

40
Q

What is statistical significance?

A

It means that the observed result is unlikely to have occurred by chance, given a significance level (e.g., p < 0.05), but it does not imply practical importance.

41
Q

What is effect size?

A

A measure of the strength of the statistical relationship, indicating the magnitude of the difference or relationship in the data, often denoted by η² (eta squared).

42
Q

What are Type I and Type II errors in hypothesis testing?

A

Type I error (α): Incorrectly rejecting the null hypothesis (false positive).
Type II error (β): Failing to reject the null hypothesis when it is false (false negative).

43
Q

What is the difference between a directional (one-tailed) and non-directional (two-tailed) hypothesis?

A

Directional (one-tailed): Specifies the direction of the relationship (e.g., “greater than”).
Non-directional (two-tailed): Does not specify the direction, just that a difference exists (e.g., “not equal to”).

44
Q

What is a Z-test?

A

A statistical test used to determine the difference between two population means when the population variances are known.

45
Q

What are the assumptions for conducting a Z-test?

A

Sample size greater than 30.
Data are independent.
Data are continuous and approximately normally distributed.
Data come from a probability sample.

46
Q

What is a one-sample T-test?

A

A parametric test used when the population standard deviation is unknown, testing whether a sample mean differs from a known or hypothesized population mean.

47
Q

What are degrees of freedom (df)?

A

The number of independent scores that are free to vary in a statistical calculation, symbolized by “df.”

47
Q

What is the purpose of a T-test?

A

To test hypotheses about mean differences between a sample and population when the population variance is unknown.

48
Q

What is the difference between a Z-test and a one-sample T-test?

A

A Z-test is used when the population standard deviation is known, while a one-sample T-test is used when the population standard deviation is unknown and is estimated from the sample.

911 (2 decks)

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