Untitled Quiz

Statistics is a field of study dealing with the collection, analysis, interpretation, presentation, and organization of data.
It is used to understand patterns, trends, and relationships within data, often used to make predictions or decisions.

Categorical Data: Data that fits into categories or groups, like gender, color, or type.
Quantitative Data: Data that can be measured numerically, like height, weight, or temperature.

Sampling is a process where a researcher selects one or more cases from a larger group (population) for study.
Important for studying populations too large to collect data on every member.
Crucial for generalizing findings to the entire population.

Simple Random Sampling (SRS): every member of the population has an equal chance of being selected. Can be with or without replacement.
Systematic Sampling: Every kth member of a population is selected.
Stratified Random Sampling: The population is divided into subgroups (strata). Then random samples are drawn from each stratum.
Cluster Sampling: A sampling method where the population is divided into groups (clusters). Then entire clusters are randomly selected.
Convenience or Accidental Sampling: The researcher selects the most accessible individuals or cases.

Questionnaires: A structured set of questions used to collect data from individuals. Can be answered in person, by mail, phone, or online.
Recording: Recording data collected through observation.
Qualitative Methods: Methods used to find information through observation, watching, listening, or reading.

n' (adjusted sample size) = n (estimated sample size without adjustment) / [1+(n/N)]
N: population size

Availability sampling: Uses readily available and accessible participants.
Snowball sampling: Participants refer other participants to take part.
Quota sampling: Samples are selected to match characteristics of the population across multiple subgroups.
Purposive sampling: Selects participants based on their specific characteristic.

A spurious relationship exists where two variables appear to have a relationship, but that relationship is actually caused by a third variable.
Controlling for other variables is important for understanding the true relationship.

Individuals: Objects or entities being observed. Can be people, animals or things.
Variables: Characteristics of an individual. Can take various values or categories.
- Quantitative: Measured numerically. Examples: Height, weight, temperature.
- Categorical: Fits into categories. Examples: Eye color, gender.
Categorical types: Nominal (unordered categories), ordinal (ranked categories)

Range: Difference between the highest and lowest values.
Interquartile Range (IQR): range between third and first quartile of a data set.
Standard Deviation: Average distance between each data point and the mean.
Variance: Sum of squares of deviations from the mean, divided by degrees of freedom.
Semi-Interquartile Deviation: Half the difference between the third and first quartiles.

Box plots visually display the five-number summary (Min , Q1, Median, Q3, Max) of a set of data.
Helpful for identifying outliers.

Outlier: An observation that is substantially different from most of the other data points.
Potential Issues
How the outlier influences the calculated mean and standard deviation.

Use the mean and standard deviation for symmetrical distributions without outliers.
Use the median for non-symmetrical distributions and those with outliers.

Table showing the possible outcomes of a hypothesis test is included.
Includes Type I and Type II errors for rejecting or accepting the null hypothesis.

Specific methods for calculating 96% and 70% confidence intervals (CI) using a given standard deviation and mean are included.
Confidence Intervals (CI) give a range within which a true population value is estimated to lie with a specified confidence level.

Explains z-scores transformation of normal distributions, and how to interpret percentiles.
Includes z-score ranges for different grades (A, B, C, etc.)
Explains how to interpret z-score values from tables of percentiles.

Sensitivity: Percentage of true positives (correct positive results).
Specificity: Percentage of true negatives (correct negative results).
Positive Predictive Value (PPV): Percentage of true positives among positive test results.
Negative Predictive Value(NPV): Percentage of true negatives among negative test results.
Important in evaluating the usefulness of tests (e.g. screening tests).