Statistics for healthcare professionals

Explain descriptive vs inferential statistics with a ten thousand student height example. Outline data types (categorical and numerical), centers and spreads, including normal versus skewed distributions.

Learn how to create and name spss variables in variable view, assign numeric types and widths, code gender with 0/1, and prepare data from Excel.

Explore jamovi basics by configuring input and output screens, creating systolic_pressure and gender variables, and converting gender to numeric values via data type and measurement type settings.

Learn to describe data in SPSS using frequency for categorical variables and descriptive statistics for numerical data, assess normality with Shapiro, and compare height and score by gender.

Use jamovi exercise 2 to describe data with gender frequency. Split by gender to describe height with mean and standard deviation, and other variable with median and quartiles.

Explore how a two by two contingency table links smoking history and ischemia incidence, reading margins and joint probabilities to assess their relationship.

Learn how relative risk and odds ratio quantify associations between exposure and disease, interpret their values, and choose appropriate measures for cohort vs case-control studies in healthcare.

Describe and analyze health data in SPSS by coding gender and disease, using descriptive, frequency, and explore, then apply crosstabs to compare groups and compute odds ratios and relative risk.

Learn jamovi exercise 3: analyze relationships between variables using frequency tables, chi-square tests, and relative risk with gender and disease data.

Learn how to estimate the population mean from samples, quantify sampling error with standard error, and construct 95 percent confidence intervals, including mean differences and relative risk or odds ratio.

Analyze the effect of corticosteroid on infection and mortality in ICU patients using SPSS, comparing 26 treated and 24 untreated individuals, and interpreting confidence intervals.

Explore using Jamovi to estimate confidence intervals for odds ratio and relative risk in assessing corticosteroid effects on infection and mortality in ICU patients, and test gender associations.

Explore hypothesis testing and type i and type ii errors, compare confidence intervals and p values, and learn when to reject the null hypothesis with alpha, power, and study design.

Link study design and research question to statistical analysis, classify questions by objective, and choose appropriate tests—parametric or nonparametric—based on group differences, prediction, and diagnostic goals.

Learn how to apply the paired t-test to a within-group before-and-after study, using blood pressure changes after joining residency to test significance with 95% CI and p-values.

Examine the effect of residency on blood pressure by creating a difference score (after minus before) and checking normality, then apply the Turkey test for a significant mean difference.

Explore residency’s effect on blood pressure with a paired t-test in jamovi exercise 5, creating a difference variable and assessing normality with histogram and QQ plots.

Explore the logic of the Wilcoxon signed-rank test for within-group pretest–posttest data, ranking differences and separating positive and negative ranks, then interpreting the p-value.

Explore the Wilcoxon signed-rank test in SPSS to compare pre and post training scores for ten residents, using related samples and legacy dialogues methods, and interpret p-values to assess significance.

Assess a new educational technique by comparing test scores of ten residents before and after a day of training in Jamovi, using a t-test, p = 0.016 indicating significance.

Analyze the logic of the unpaired t test by comparing two diet groups on weight loss, using 95% confidence intervals and hypothesis testing to detect differences.

Compare weight changes in low carbohydrate vs low fat diets and cholesterol changes in drug vs placebo using SPSS, with normality, outlier, and variance checks, finding significant differences.

Explore how Jamovi 7 & 8 guide two-sample t-tests and the assessment of normality, outliers, and equal variances in diet and cholesterol case studies.

Explain the logic of the two-sample rank test for comparing traditional and peer-to-peer training using an ordinal score. Learn how ranking, mean ranks, and p values indicate significance.

Compare traditional and peer-to-peer training on test scores using SPSS nonparametric independent samples and legacy dialogues, interpreting p-values and descriptive statistics.

Jamovi exercise 9 compares traditional training and peer to peer training using an independent samples t-test, showing median scores for peer to peer training and a significant p-value around 0.03.

Compare three or more groups with one-, two-, and three-way anova, covering between, within, and mixed designs and guarding against type i error.

Perform a one-way analysis of variance in SPSS to compare LDL across groups (control, drug E, drug B), check normality, outliers, and equal variances, then use Tukey post hoc tests.

Jamovi exercise 10 analyzes LDL levels across control, drug E, and drug B groups with a one-way ANOVA, checks normality and equal variances, and uses Tukey post hoc tests for significance.

Apply the Kruskal-Wallis test to compare three training groups—traditional, peer-to-peer, and hands-on—on ordinal test scores using ranks and mean ranks, testing equal ranks versus differences.

Apply Kruskal testing in SPSS across three groups: traditional, hands-on, and peer-to-peer. Use independent samples and legacy dialogues, mark group as nominal, transfer test scores, and interpret p-values.

Jamovi exercise 11 compares traditional, peer-to-peer, and hands-on training on test scores using descriptive analysis, percentiles, and a nonparametric one-way test with pairwise results indicating significant differences.

Learn the logic of chi-square tests for two categorical variables, compute expected counts, and compare relative risk and odds ratios to choose between chi-square and Fisher exact.

An SPSS exercise demonstrates chi-square tests and crosstabs for two- and three-category data, comparing beta blocker and aspirin effects on heart attack incidence with post hoc comparisons.

Analyze how beta blockers relate to myocardial ischemia and how aspirin affects heart attack risk using Jamovi, with chi-square, odds ratio, and relative risk calculations.