Biostatistics for Biomedical Researchers

10 Questions

Which type of data uses nonparametric methods?

Discrete data

Which statistical method is used to summarize data using indexes like mean and median?

Descriptive statistics

Which statistical test is used for paired data?

Paired samples t-test

Which statistical software can aid in analysis?

SPSS

Which measure is used to assess diagnostic accuracy?

Sensitivity

Which type of data can use both parametric and nonparametric methods?

Continuous data

Which statistical method is considered stronger but has strict assumptions?

Parametric methods

Which test is used to test change in proportions between two paired groups?

McNemar test

Which statistical method uses median and interquartile range?

Nonparametric methods

Which type of data has no alternative for comparing proportions?

Nominal data

Study Notes

Selecting appropriate statistical methods is crucial in biomedical data analysis.
Incorrect selection can lead to problems in interpretation and affect study conclusions.
Statistical methods are chosen based on the aim and objective of the study, type and distribution of data, and nature of observations.
Descriptive statistics summarize data using indexes like mean and median, while inferential statistics draw conclusions using tests like t-test and ANOVA.
Nominal, ordinal, and discrete data use nonparametric methods, while continuous data can use parametric and nonparametric methods.
Paired data use paired samples t-test, while unpaired data use independent samples t-test.
Parametric tests assume continuous data that is approximately normally distributed, while nonparametric tests can be used for non-normal or other types of data.
Parametric tests have nonparametric counterparts that can be used as backup analysis.
Student's t-test and F-test are considered parametric tests, while Pearson correlation is a common parametric method.
SPSS, R, Stata, and SAS are statistical software that can aid in analysis, but selection of appropriate statistical test remains a difficult task for non-statistical researchers.
Parametric methods use mean and standard deviation, while nonparametric methods use median and interquartile range.
Nonparametric methods have no alternative for comparing proportions.
Logistic regression analysis, survival analysis, and receiver operating characteristics curve are semiparametric or nonparametric methods.
Sensitivity, specificity, and overall accuracy are key measures used to assess diagnostic accuracy.
Parametric methods are stronger but have strict assumptions, while nonparametric methods are preferred when assumptions are not met.
Outliers affect mean in parametric methods but not median in nonparametric methods.
For small sample size, normality testing methods are less sensitive about non-normality.
For large sample size, most statistical methods are highly sensitive about non-normality.
McNemar test is used to test change in proportions between two paired groups, while Cochran Q test is used for three or more paired groups.
Intraclass correlation coefficient is calculated when both pre-post data are in continuous scale.
Selection of appropriate statistical methods is crucial for producing valid and reliable research results.
Different statistical tests make specific assumptions about the data and should be chosen accordingly.
Incorrect or inappropriate use of statistical methods can lead to defective conclusions and harm evidence-based practices.
Basic knowledge of statistics and appropriate selection of statistical methods are important for producing quality biomedical research.
There is no fixed minimum or maximum sample size for any particular statistical method and it is subject to estimation based on various factors.
Only with sufficient sample size can significant differences be detected.
Wrong selection of statistical methods can affect the significance level and conclusions of a study.
Consultation from statistical experts may be necessary for appropriate selection of statistical methods.
Biomedical researchers and academicians may find it difficult to learn all statistical methods, but basic knowledge is important.
Proper use of biostatistical methods ensures the highest quality of statistical reporting in research and journals.