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  • 19

    Guide to Essential Biostatistics XXI: A Short History of Applied BioStatistics

    In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. The first quarter of the 20th century was an intense period of […]

  • 19

    Guide to Essential Biostatistics XX: Chi-square test: goodness of fit for sample validation

    In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. The chi-squared distribution or χ2-distribution, developed by English mathematician and statistician Karl […]

  • 21 (2)

    Guide to Essential Biostatistics XIX: Linear regression (PROBIT)

    In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. Probit (“probability unit”) models were developed by American biologist and statistician Chester […]

  • 20 (2)

    Guide to Essential Biostatistics XVIII: Non-linear regression (Sigmoidal dose-responses

    In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. Bioassays (analytical methods to determine potency of a substance by its effect […]

  • STAT17

    Guide to Essential Biostatistics XVII: Inferential Statistics – post ANOVA tests

    In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test). Post-ANOVA tests are typically performed after the f-ratio has indicated […]


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