Statistics Flashcards
What is Data Analysis? (Data Analysis)
Data analysis involves examining, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.
Characteristics of Qualitative Data (Qualitative vs Quantitative)
Qualitative data encompasses non-numerical, descriptive information, often collected through methods like interviews or observations, and is analyzed for patterns or themes.
Characteristics of Quantitative Data (Qualitative vs Quantitative)
Quantitative data consists of numerical information that can be measured and compared statistically, often used to quantify behavior, opinions, or other defined variables.
Why are Statistics Important in Quantitative Analysis? (Statistics)
Statistics provide a framework for making inferences about a population based on sample data, allowing researchers to quantify confidence in their findings.
What is a Confidence Interval? (Statistics - Confidence Intervals)
A confidence interval is a range of values, derived from sample statistics, that is believed to contain the value of an unknown population parameter with a certain probability.
What is Nominal Data? (Nominal Data)
Nominal data represents categories without a natural order or ranking among them, such as types of ice cream flavors or car brands in a survey.
What is Ordinal Data? (Ordinal Data)
Ordinal data consists of categories with a natural order but without a consistent scale between them, such as satisfaction ratings (satisfied, neutral, dissatisfied).
Difference Between Interval and Ratio Data (Interval and Ratio)
Interval data has ordered categories with a known distance but no true zero point (e.g., temperature), while ratio data has a true zero and can express a ‘twice as much’ relationship (e.g., weight).
How to Perform Thematic Analysis (Thematic Analysis)
Thematic analysis involves coding qualitative data, identifying patterns, and forming themes to interpret and report findings, requiring immersion in the data and iterative analysis.
Ethical Considerations in Data Analysis (A Very Ethical Slide)
Researchers must strive to minimize bias, maintain transparency, and ethically handle data, ensuring that the interpretation and reporting of results are honest and unbiased.
