1. Supervision/support of scientific projects

We provide supervision/support of any scientific project (definition of the research plan, defining a sample/population, hypothesis, measuring instruments, testing of metric characteristics of instruments, collecting a data, reduction of data, data analysis, dissemination )

Supervision and support of scientific projects are critical aspects of ensuring their success. Whether in academia, industry, or other research settings, effective supervision and support contribute to the quality of the research and the development of researchers. Here are key elements of supervision and support in scientific projects:

  1. Project Planning and Design:
    o Define Objectives: Clearly articulate the goals and objectives of the scientific project.
    o Research Design: Collaborate with researchers to develop a robust research design and methodology.
  2. Resource Allocation:
    o Budgeting: Assist in budget planning, resource allocation, and securing funding when necessary.
    o Facilities and Equipment: Ensure access to necessary facilities, equipment, and technology.
  3. Team Building:
    o Team Formation: Assist in assembling a competent and diverse research team.
    o Roles and Responsibilities: Clearly define roles and responsibilities within the research team.
  4. Mentorship:
    o Guidance: Provide guidance on research methodologies, data analysis, and interpretation of results.
    o Career Development: Support the career development of researchers, including guidance on publications, presentations, and networking.
  5. Ethical Oversight:
    o Compliance: Ensure adherence to ethical guidelines and regulatory requirements.
    o Institutional Review Board (IRB): Facilitate the IRB approval process for projects involving human subjects.
  6. Progress Monitoring:
    o Regular Meetings: Schedule regular meetings to assess project progress and address any challenges.
    o Feedback: Provide constructive feedback on research methodologies, data collection, and analysis.
  7. Collaboration:
    o Interdisciplinary Collaboration: Encourage collaboration with experts from diverse fields to enrich the research.
    o Networking: Facilitate connections with other researchers, institutions, and industry partners.
  8. Problem-Solving:
    o Troubleshooting: Assist in identifying and resolving issues that may arise during the course of the project.
    o Adaptability: Encourage flexibility and adaptability in response to unexpected challenges.
  9. Communication:
    o Dissemination: Support the dissemination of research findings through conferences, publications, and other channels.
    o Stakeholder Engagement: Facilitate communication with stakeholders, including funders, policymakers, and the public.
  10. Project Closure:
    o Evaluation: Conduct a thorough evaluation of the project’s outcomes and impact.
    o Documentation: Ensure proper documentation of methodologies, results, and any lessons learned.
    Effective supervision and support create an environment that fosters innovation, collaboration, and the successful execution of scientific projects.

Upgrade and statistical supervision

  1. Upgrade and statistical supervision
    Upgrade and statistical supervision of existing research (inferential statistics, bayesian inference, controlling bias, residuals, errors, improvement predictions/linear and non-linear tests, testing an assumption for used tests, transformation data…)

Inferential statistics is a branch of statistics that involves using data from a sample to make inferences or draw conclusions about a population. The primary goal of inferential statistics is to generalize findings from a sample to a larger population while taking into account the inherent uncertainty and variability in the data. This process is essential when it’s impractical or impossible to study an entire population.
Key concepts and techniques in inferential statistics include:

  1. Population and Sample:
    o Population: The entire group of individuals or observations that the researcher is interested in studying.
    o Sample: A subset of the population from which data is collected.
  2. Sampling Methods:
    o Random Sampling: Ensuring each member of the population has an equal chance of being included in the sample.
    o Stratified Sampling: Dividing the population into subgroups (strata) and then randomly sampling from each subgroup.
  3. Statistical Inference:
    o Estimation: Using sample data to estimate population parameters (e.g., mean, proportion).
    o Hypothesis Testing: Assessing the likelihood that observed differences or associations in the sample are reflective of true differences or associations in the population.
  4. Confidence Intervals:
    o Providing a range of values (interval) within which the true population parameter is likely to fall with a certain level of confidence.
  5. Hypothesis Testing Steps:
    o Formulating a null hypothesis (H0) and an alternative hypothesis (Ha).
    o Selecting a significance level (alpha, often set at 0.05).
    o Collecting data and calculating a test statistic.
    o Comparing the test statistic to a critical value or determining the p-value.
    o Drawing conclusions about the null hypothesis.
  6. Type I and Type II Errors:
    o Type I Error: Incorrectly rejecting a true null hypothesis (false positive).
    o Type II Error: Failing to reject a false null hypothesis (false negative).
  7. P-Value:
    o The probability of obtaining results as extreme as those observed in the sample, assuming the null hypothesis is true.
  8. Statistical Tests:
    o Parametric Tests: Assume certain characteristics about the population distribution (e.g., t-test, ANOVA).
    o Nonparametric Tests: Do not make assumptions about the population distribution (e.g., Mann-Whitney U test, Wilcoxon signed-rank test).
  9. Multivariate/Univariate/Robust tests
    Inferential statistics plays a crucial role in scientific research, allowing researchers to make broader conclusions based on limited data. However, it also involves potential sources of error and uncertainty that need to be carefully considered in the interpretation of results.