250128L - BIOSTATISTICS FOR CAPSTONE 2 STUDENTS

Presented at the Nursing College PNU on 28 January 2025 2.00 pm by Professor Omar Hasan Kasule MB ChB (MUK), MPH (Harvard), DrPH (Harvard)

DATA ANALYSIS

• Variables discrete (categorical/counted) vs continuous (measured)
• The final output of all analysis is the p value. 
• P<0.05 significant difference. P>0.05 no significant difference
• Tests for association (t, F, Chi, linear regression coefficient)
• Test for effect: odds ratio, logistic regression coefficient

TESTS OF ASSOCIATION FOR CONTINUOUS DATA

• T-test: for 2 groups of continuous variables
• F test: for 3 or more groups of continuous variables
• Correlation coefficient: for 2 groups of continuous data
• Linear regression: association between one continuous variable and several continuous variables

TESTS OF ASSOCIATION FOR DISCRETE DATA

• Chis-square: for 2 or more groups of discrete data
• Binary logistic regression: association between one discrete variable and several discrete variables

WRITING THE RESULTS SECTION-1

• The results section presents the findings of the procedures carried out in the methods section. It should be brief and to the point.
• A distinction must be made between results and data. Result refers to summary information obtained from data analysis. Data is the actual numerical information often presented in a summarized form. The result is presented followed by presentation of supporting data.
• Results of hypothesis-based studies should be in the past tense. Data of descriptive studies should be in the present tense.
• Data are presented in the form of tables and diagrams (figures, bar diagrams, graphs, pie-charts, maps etc.). – number of figures depends on the journal.
• Presentation of numerical data in text should be kept to a minimum.

WRITING THE RESULTS SECTION-2

• Only results relevant to the research hypothesis should be presented. Both negative and positive results are presented. It is considered scientific fraud to present only those results that the author thinks favor a particular hypothesis.
• The results section is written in order. The most important results are presented before the least important.
• Emphasis can be put on some results and not others. Not all the data from the study need be reported. 
• Citing data in the text takes less space but is more difficult to read.

WRITING THE RESULTS SECTION-3

• Magnitude of change should be presented as a summary statistic such as percentage change instead of presenting the raw data.
• Summary statistics normally used as the mean, the median, and the proportion. The mean should be presented properly as mean +/- standard deviation or standard error of the mean (SD or SE) with units of measure indicated.
• Measures of effect are normally the chi-square and the t statistics. Actual p values should be given instead of indicating <0.05 or >0.05.

WRITING THE RESULTS SECTION-4

• The following types of figures are used: line graph, scatter gram, bar graph, histogram, and the frequency polygon.
• The title of the figure should reflect its contents.
• The figure must be labeled correctly.
• Symbols must be defined.
• The names of variables and units of measurement must be labeled appropriately.
• Tables must be properly titled and column headings clearly indicated.
• Footnotes, subscripts, and superscripts can be used.

PROBLEMS TO AVOID IN THE RESULTS SECTION

• Failure to report baseline characteristics. 
• Selective reporting of favorable results. 
• Numerators without denominator. Inappropriate denominators. 
• Numbers that do not add up. 
• Tables not labeled properly or completely. 
• Numerical inconsistency (rounding, decimals, and units). 
• Stating results as mean +/- 2SD for non-normal data. 
• Stating p values as inequalities instead of the exact values. 
• Missing confidence limits. 
• Reporting results both in the text and the tables or figures. 
• Inconsistencies between text and table data.

PROBLEMS TO AVOID IN THE DISCUSSION SECTION

• Repeating the results section. 
• Not comparing with similar studies
• Failure to explain atypical results
• Failure to discuss the consistency of conclusions with the data and the hypothesis. 
• Extrapolations beyond the data. 
• Failure to discuss short-comings and limitations of the study. 
• Failure to evaluate statistical conclusions in view of testing errors. 
• Failure to assess bias (misclassification, selection, and confounding).
• inadequate sample size, and inadequate power