Research Methods and Biostatistics Overview

Research Methods and Biostatistics

• This lecture series covers research methods and biostatistics, specifically for undergraduate students.
• The instructor is Dr. Walhan Alshaer, Director of Pharmacological and Diagnostic Research Center at Al-Ahliyya Amman University (AAU) and Senior Research Scientist at the Cell Therapy Center, The University of Jordan.

Introduction to Biostatistics

• Descriptive Statistics: Focuses on summarizing data, using measures of central tendency (mean, median, mode) and variability.
• Inferential Statistics: Draws conclusions about a population based on sample data, including hypothesis testing, population vs. sample concepts.

Descriptive Statistics vs. Inferential Statistics

• Descriptive Statistics: Summarizes data.
• Inferential Statistics: Makes conclusions, predictions, and uses sample data to estimate population characteristics. It is essential in research, clinical trials, and quality control.

Key Concepts in Inferential Statistics: Population vs. Sample

• Population: The entire group of interest in a study
• Sample: A smaller group selected from the population
• Example: Population: All diabetic patients in a city; Sample: 150 randomly chosen diabetic patients.

Inferential Statistical Methods: Estimation and Confidence Intervals

• Point Estimate: A single value used to estimate a population parameter (e.g., sample mean).
• Confidence Interval (CI): A range of values likely containing the population parameter based on sample data - a 95% CI.
• Formula for CI for the mean: CI = x̄ ± z*(s/√n), where: x̄ is sample mean, z is confidence level value, s is standard deviation, and n is sample size.
• Example: Calculation of a 95% confidence interval given sample data.

Inferential Statistical Methods: Hypothesis Testing

• Definition: A method to test claims or hypotheses about population parameters.
• Steps:
  • State null and alternative hypotheses (H₀ and H₁).
  • Set significance level (α = 0.05).
  • Select test statistic.
  • Compute the test statistic and p-value.
  • Compare p-value with α. If p<α, reject H₀.
  • Interpret results.
• Example: Testing if a drug reduces blood pressure.

Applications of Inferential Statistics

• Medical Research: Clinical trials for new vaccines.
• Business Analytics: Customer satisfaction, sales predictions.
• Environmental Science: Pollution level analysis.
• Quality Control: Checking for defective products.

Advantages and Limitations of Inferential Statistics

• Advantages: Allows decision-making with incomplete data, produces estimates and confidence about population characteristics.
• Limitations: Accuracy depends on sampling methods, sampling errors and biases can lead to wrong results.

Discussion

• P-value interpretation: How to interpret p-values (0.03 and 0.06) in hypothesis testing.

Statistical Analysis Techniques

• Parametric vs. Non-Parametric Tests:
  • Parametric tests assume specific data distributions (usually normal), while non-parametric tests don't make these assumptions.
  • Parametric examples include t-tests (independent and paired), ANOVA (one-way and two-way), and regression analysis (simple and multiple).
  • Non-parametric tests for alternatives include Mann-Whitney U, Wilcoxon Signed-Rank, Kruskal-Wallis, and Spearman correlation.
  • Selection depends on data type and distribution.

Correlation and Regression Analysis

• Correlation: Measures strength and direction of relationship between two variables.

  • Pearson correlation coefficient (r): Used for continuous data meeting normality assumptions, ranges from -1 to +1.
  • Spearman rank correlation: Used for ordinal data or when normality assumptions are violated. Based on ranks rather than raw values.

• Regression Analysis: Models the relationship between variables to predict outcomes.

  • Simple Linear Regression: One independent variable predicts one dependent variable.
  • Multiple Linear Regression: Two or more independent variables predict one dependent variable

Practical Applications of Statistical Techniques

• Medical Research: T-tests and regression for clinical outcomes.
• Social Sciences: Chi-square tests.
• Business Analytics: Multiple regression for sales predictions.
• Environmental Studies: Correlation analysis for pollutant levels and health outcomes.

Discussion: Summary

• Parametric tests are powerful but need assumptions.
• Non-parametric tests are flexible, suitable for non-normal data.
• Correlation quantifies relationships, and Regression predicts and determines variable impacts.
• Q&A covering t-test assumptions and Spearman vs. Pearson correlation use.

Next Lecture: Interpreting and Presenting Data

• This lecture will cover practical applications of statistical software, data entry/management, data visualization, presenting findings, understanding p-values/confidence intervals, making inferences from data.

Explore the key concepts of research methods and biostatistics tailored for undergraduate students. This quiz covers essential topics like descriptive and inferential statistics, providing a solid foundation for those interested in the field. Test your understanding of how these statistical methods apply to research and clinical trials.