Blog - 57

𝐇𝐨𝐰 𝐭𝐨 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐟𝐨𝐫 𝐇𝐞𝐚𝐥𝐭𝐡 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 📊🩺

Statistical methods play a crucial role in health research, ensuring data is analyzed correctly for accurate and reliable conclusions. Selecting the right method depends on the study design, data type, and research objectives. 📑🔍

1️⃣ Identify Your Research Question 🧐

Descriptive: Summarizing data (e.g., mean, median, mode, standard deviation, variance).
Inferential: Making predictions or testing hypotheses (e.g., t-tests, regression, confidence intervals, p-values).
Comparative: Evaluating differences between groups (e.g., ANOVA, chi-square test, effect size).

2️⃣ Understand Your Data Type 📊

Continuous Data: Height, weight, blood pressure ➝ Use parametric tests like t-tests, ANOVA, Pearson correlation.
Categorical Data: Gender, disease status ➝ Use non-parametric tests like chi-square tests, Fisher’s exact test.
Ordinal Data: Pain scores, satisfaction levels ➝ Use Mann-Whitney U test, Kruskal-Wallis test, or ordinal logistic regression.

3️⃣ Choose the Right Statistical Method 🔬

For Correlation: Pearson’s or Spearman’s correlation with r-value interpretation.
For Relationship Analysis: Regression models (linear, logistic, Cox proportional hazard, R-squared value, odds ratio).
For Group Comparisons: t-tests (two groups) or ANOVA (multiple groups), effect size calculation.
For Time-to-Event Data: Kaplan-Meier survival analysis, Cox regression, hazard ratios.

4️⃣ Consider Sample Size & Distribution 📏

Large Sample & Normal Distribution ➝ Parametric methods (central limit theorem applies).
Small Sample or Skewed Data ➝ Non-parametric methods (e.g., Wilcoxon signed-rank test).
Check for Assumptions: Normality (Shapiro-Wilk test, Kolmogorov-Smirnov test), Homogeneity (Levene’s test, Bartlett’s test).
Power Analysis: Determines the minimum sample size required for statistical significance.

5️⃣ Use Statistical Software 🖥️

SPSS: User-friendly, widely used in health research.
R: Flexible, powerful for advanced statistical analysis, reproducible research.
STATA: Ideal for epidemiological studies and survival analysis.
Python: Great for data science and machine learning applications, using libraries like statsmodels and scipy.stats.

🎯 Take Action Now!

📊 Choose appropriate statistical tools for accurate health research.

🏥 Utilize statistical software for robust data analysis.

📚 Educate yourself on statistical principles to enhance research credibility.

🧐 Seek expert consultation for complex statistical modeling.

🔬 References