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    FINER and PICO

    Comparator FINER Hypothesis Testing Intervention Outcome PICO Population Post-positivism Research Design Research Question Sampling Method Statistical Power Analysis Statistics

    An amalgamation of philosophy and objectivity

    The research question is the foundation of everything empirical

    Research questions (and answering them) are always the primary focus of anything and everything empirical, methodological, epidemiological, and statistical. Without a research question, there is no reason to conduct a study or run statistics.

    The following are DIRECTLY derived from research questions:

    1. Null and alternative hypotheses (hypothesis testing and inferential statistics)
    2. Research design (observation or experimental)
    3. Population of interest (inclusion and exclusion criteria) 
    4. Sampling method (non-probability or probability)
    5. Intervention or independent variable (categorical, ordinal, or continuous)
    6. Confounding or control variables (secondary, tertiary, and ancillary research questions)
    7. Comparator or control treatment (categorical, ordinal, or continuous)
    8. Outcome or dependent variable (categorical, ordinal, or continuous)
    9. Outcome and design for an a priori power analysis to calculate sample size
    10. Structure of the database (between-subjects, within-subjects, or multivariate) and code book
    11. Statistical tests used (descriptive, between-subjects, within-subjects, correlations, survival, or multivariate)

    Researchers must take the appropriate amount of time to fully formulate and refine research questions. SO MUCH is dependent upon it for their study. Luckily, this task is made easier with the use of two prevalent mnemonics: FINER (feasible, interesting, novel, ethical, relevant) and PICO (population, intervention, comparator, outcome).

    FINER is a more of a philosophy for writing research questions. The arguments for the "F," "I," "N," "E," and "R" are all and informed upon by the empirical literature in the area of empirical or clinical interest. Researchers especially have to be well vested in the most current literature in order to make sound arguments for interesting, novel, and relevant questions.

    PICO is employed to explicitly and operationally define the population of interest, the intervention, the comparator, and the outcome in a research question. It is also more readily applicable in busy clinical and empirical environments and when writing literature search queries.  

    These two mnemonics compliment each other very well in applied empirical and clinical environments. The post-positivist philosophy of social and medical sciences lends itself well to FINER. Measurement of observable constructs and the application of experimental designs through the PICO mnemonic is also strongly reflective of a post-positivist philosophical orientation. Together, the "why" and "what" questions associated with conducting research can be argued in an evidence-based, objective, and logically sound fashion.
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    95% confidence intervals

    95% Confidence Interval Adjusted Odds Ratio Confidence Interval Hazard Ratio Measurement Odds Ratio With 95% CI P-value Relative Risk Statistics

    Precision and consistency of treatment effects

    95% confidence intervals are dependent upon sample size

    If there is ANY statistical calculation that holds true value for researchers and clinicians on a day-to-day basis, it is the 95% confidence interval wrapped around the findings of inferential analyses. Statistics is not an exact mathematical science as far as other exact mathematical sciences go, measurement error is inherent when attempting to measure for anything related to human beings, and FEW tried and true causal effects have been proven scientifically. Statistics' strength as a mathematical science is in its ability to build confidence intervals around findings to put them into a relative context.  

    Also, 95% confidence intervals act as the primary inference associated with unadjusted odds ratios, relative risk, hazard ratios, and adjusted odds ratios. If the confidence interval crosses over 1.0, there is a non-significant effect. Wide 95% confidence intervals are indicative of small sample sizes and lead to decreased precision of the effect. Constricted or narrow 95% confidence intervals reflect increased precision and consistency of a treatment effect.

    In essence, p-values should not be what people get excited about when it comes to statistical analyses. The interpretation of your findings within the context of the subsequent population means, odds, risk, hazard, and 95% confidence intervals IS the real "meat" of applied statistics.
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    Preliminary statistical consultation

    Database Management Research Engineer Sample Size Statistical Analysis Statistical Consultation Statistician Statistics

    Support your local statistician!

    Seek out methodological and statistical consultation

    If you have access to a statistical consultants or statisticians within your empirical or clinical environment, seek out their services in the preliminary phases of planning your study. Here is a list of things that I do for residents, fellows, faculty, physicians, pharmacists, nurses, and staff at an academic regional medical campus:

    1. Sample Size - I conduct sample size calculations for at least of 80-85% of my first-time clients. They often want to know how many people they need to reach a significant p-value. We work through the process of acquiring an evidence-based measure of effect that reflects what their research question is trying to answer.

    It feels good knowing that you have a good chance of detecting significance with a small sample size. Also, it is good to find out that you have to collect A LOT more observations than you thought you would. Post hoc power analyses should be run for any non-significant main effects that may be considered Type II errors (limited or small sample sizes).

    2. Statistical analysis - Real biostatistical scientists and statisticians will conduct your statistical analyses in an objective and expeditious manner to help you answer your research questions. Please help them understand what your research question is and what research design you want to use to answer it to the best of your abilities. They will be able to help you choose the correct statistic given that you can tell them the scale of measurement for your primary outcome and what type of design (between-subjects, within-subjects, correlational, mixed, or multivariate) you want to use to answer your question. It is also important to know WHO or WHAT you want to include in your sample in terms of inclusion and exclusion criteria. Finally, know your content area. We may not know your knowledge/philosophical base and need to understand the entire picture, as much as you can tell us.

    3. Database management - Go ahead and let us build your database in a basic Excel spreadsheet and send an accompanying code book in Word so that we are all on the same page. It helps us all know what is going on, what variables are being collected, what they mean, how they are measured, and how the analysis will work. Share it with all members of the research team. Use the code book when entering your data. Tell the rest of us if you make changes to the code book or database. These simple tasks and communicative efforts can mean the difference between your statistics being run in five minutes versus five weeks.  SERIOUSLY.

    4. Write-up of findings for publication - We will give you an annotated write-up of your findings with statistical outputs and give you basic and unbiased interpretations of the statistical results of your study. We can help you write up the statistical methods and results sections of your abstracts and manuscripts. We can even help you design tables and graphs that will make your study findings more aesthetically and visually appealing to your audience.

    When it comes to authorship, if you feel that your statistical professional's contribution to the design, execution, and interpretation of your study warrants authorship, offer it to them. They will greatly appreciate it! However, YOU SHOULD NEVER BE REQUIRED TO GIVE US AUTHORSHIP JUST BECAUSE WE RAN YOUR STATISTICS FOR YOU.  IT IS UNETHICAL FOR US TO REQUIRE AUTHORSHIP FOR DOING OUR JOB. THAT IS, IF OUR JOB IS TO RUN STATISTICS IN YOUR EMPIRICAL OR CLINICAL ENVIRONMENT.          
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    Statistical tests

    Hypothesis Testing Research Question Statistics

    Statistical tests are used to answer research questions

    It's not about the statistics, it's about the question.

    In my experience, statistics is a cognitive dissonance-inducing mathematical science and no one tends to recall their personal and professional statistical experiences with much zeal. It's as if there is an automatic recoil when the topic of statistics enters the discussion and planning of a research study. The literature has posited that statistics are intimidating and nebulous because many people do not possess the necessary competencies and experience with statistics and also people do not understand the lexicon of the science.

    The most important thing to remember about applied statistics, despite their prevalence, relevance, and utility in everyday life, is that they are tools that human beings use to communicate the results of data analysis. Hypothesis testing is employed in empirical research so that researchers can present their findings in a relative context that is interpretable and applicable in other research and applied environments.

    Statistics are useful ONLY when they are used to answer useful, appropriate, answerable, relevant, and valid research questions that are grounded in the empirical literature.