Week 1: Introduction to Statistics

Week 1: Introduction to Statistics

• Statistics is the science of collecting, organizing, analyzing, and interpreting numerical data to make informed decisions.
• Statistics is used in healthcare, research, business, education, and government.
• Examples include weather forecasts, patient blood pressure trends, and poll results.

Levels of Measurement

• Nominal Scale: Categorizes data without numerical ranking (e.g., blood type).
• Ordinal Scale: Orders data but does not quantify differences between ranks (e.g., pain levels).
• Interval Scale: Quantitative scale with equal intervals but no true zero (e.g., temperature in Celsius).
• Ratio Scale: Quantitative scale with a true zero, allowing for meaningful ratios (e.g., weight in kilograms).

Variables in Research

• Independent Variable (IV): The variable manipulated or categorized to observe its effect (e.g., type of diet).
• Dependent Variable (DV): The outcome measured (e.g., patient weight loss).
• Qualitative Variables: Non-numerical data (e.g., gender, type of medication).
• Quantitative Variables: Numerical data (e.g., age, cholesterol level).
• Discrete Variables: Countable values (e.g., number of hospital visits).
• Continuous Variables: Any value within a range (e.g., time spent exercising).

Descriptive vs. Inferential Statistics

• Descriptive Statistics: Summarizes data (e.g., mean, median, mode).
• Inferential Statistics: Draws conclusions or makes predictions based on data (e.g., “A new treatment significantly reduces blood pressure”).

How Statistics Can Be Misleading

• Misinterpretation of graphs (e.g., truncated axes).
• Poor sampling techniques (e.g., biased surveys).
• Cherry-picking data or presenting averages without context.

Week 2: Visualizing Data

• Frequency Tables: Organize data into categories and count occurrences.
• Graphs: Visual summaries of data.

Different Ways to Visualize Data

• Pie Charts: Show proportions of a whole.
• Bar Charts: Visualize differences between categories.
• Stem-and-Leaf Displays: Represent numerical data in a way that reveals the data distribution.

Graphing Distributions

• Histograms: Group data into intervals (bins) to show the shape of the distribution.
• Box Plots: Display data spread and identify outliers.
• Line Graphs: Show changes over time.
• Scatterplots: Visualize relationships between two variables.

Week 3: Descriptive Statistics, Normal Curve, Percentiles, Probability, Central Tendency & Variation

• Descriptive Statistics: Summarizes data to make it interpretable, using measures like central tendency (mean, median, mode) and variation (range, standard deviation).
• Normal Distribution: Symmetrical bell-shaped curve, with specified percentages of data falling within certain standard deviation ranges.
• Skewness: A measure of asymmetry in a distribution; positive skew indicates that the tail is on the right, while negative skew indicates that the tail is on the left.
• Probability: Likelihood of an event occurring.

Percentiles

• Represent position in a data set relative to other data points.

Types of Distributions

• Unimodal: One peak.
• Bimodal: Two peaks.
• Multimodal: More than two peaks.
• Cumulative Frequency: Running total of frequencies.
• Cumulative Percentage: Percentage of data points below a given value.

Week 4: Sampling, Sample Size, Reliability, & Validity

• Probability Sampling: Every member of the population has an equal chance of being selected.
• Non-Probability Sampling: Participants are selected based on convenience or judgment.
• Sampling Error: Difference between a sample statistic and the true population parameter due to random chance.
• Sampling Bias: Systematic error introduced by the sampling method.
• Inclusion Criteria: Characteristics participants must have to be eligible for the study.
• Exclusion Criteria: Characteristics that disqualify participants from the study.
• Sample Size and Power Analysis: Method to determine the minimum sample size needed to detect a significant effect.
• Reliability: Consistency of a measurement.
• Validity: Accuracy of a measurement.

Week 5: Hypothesis Testing & t-tests

• Hypothesis Testing: A statistical method to make decisions about population parameters based on sample data.
• Null Hypothesis: Assumes no effect or difference exists.
• Alternative Hypothesis: Proposes an effect or difference exists.
• Type I Error: Incorrectly rejecting the null hypothesis (false positive).
• Type II Error: Failing to reject the null hypothesis when it is false (false negative).
• Statistical Significance: A result is unlikely to have occurred by chance.
• Clinical Significance: Practical and meaningful implications for patient care.
• t-Tests: Used to compare means of two groups.

Week 6: Comparing Independent & Dependent Samples

• Independent Samples t-test: Used when comparing means of two unrelated groups.
• Dependent (Paired) Samples t-test: Used when comparing means of the same group at two different points in time (pre-test/post-test).
• Conditions for t-tests: Data approximately normally distributed, continuous outcome variable.
• Homogeneity of variance: Samples have similar variances.

Week 7: Analysis of Variance (ANOVA)

• ANOVA tests for differences between means across 3 or more groups.
• One-Way ANOVA: Used for single independent variable.
• Repeated-Measures ANOVA: Used when evaluating changes within a group over time.
• Post-Hoc Tests: Conduct pairwise comparisons after significant ANOVA results.
• Assumptions for ANOVA: Data are approximately normally distributed, variances across groups are equal.

Week 8: Chi-Square Test

• The Chi-Square test is used for analyzing categorical data.
• Goodness-of-Fit Test: Determines if observed frequencies match expected frequencies for a single categorical variable.
• Test of Independence: Examines the relationship between two categorical variables.
• Homogeneity Test: Compares distributions of a categorical variable across different groups.

Week 9: Correlations & Regression Analysis

• Correlation: Measures the strength and direction of a relationship between two continuous variables.
• Linear Regression: Predicts value of a dependent variable given one independent variable(e.g., predicting patient weight based on caloric intake).
• Multiple Regression: Predicts value of a dependent variable given multiple independent variables(e.g., predicting hospital lengths of stay).
• Logistic Regression: Predicts probability of a binary/categorical outcome variable(e.g., predicting whether a patient will develop diabetes).

Week 10: Sensitivity/Specificity, Odds Ratio, Relative Risk, & Logistic Regression

• Sensitivity and Specificity: Measure how accurately a test identifies presence or absence of a condition.
• Odds Ratio: Measures the odds of exposure among cases compared to controls.
• Relative Risk: Measures how much the risk of a condition is increased by exposure.
• Confidence Interval: Range of values within which the true population parameter is likely to fall.
• Logistic Regression: Predicts a categorical outcome (e.g., disease/no disease) based on several inputs.

Week 11: Reading & Understanding Statistics in Nursing Research

• Evaluating statistics in nursing research: Involves checking the appropriateness of statistical methods, if results answer research question(s), and sample size adequacy.
• Common statistical measures: Descriptive statistics (summarize data), inferential statistics (test hypotheses).
• Using statistics in nursing, beyond bedside: Use statistics for education, policy, and research to improve patient care.
• Statistical significance vs. clinical significance: Statistical significance refers to findings unlikely to be due to chance; clinical significance implies practical importance to patient care.

This quiz covers the fundamentals of statistics, including its definition and applications across various fields such as healthcare and business. It also introduces different levels of measurement and the types of variables in research. Test your understanding of these essential statistical concepts.