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What do you do if your statistical experimental design and analysis needs feedback?

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When you're deep into the world of statistics, designing an experiment or running an analysis can sometimes feel like navigating a labyrinth. You've set your hypotheses, chosen your methods, and crunched the numbers, but what if you're unsure about the results? Seeking feedback is crucial, but it's not always clear how to go about it. Whether you're a student, a researcher, or a professional analyst, knowing how to get and utilize feedback on your statistical work can make the difference between robust findings and misguided conclusions.

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Yogita Kolekar Thoke??

?Global Biostatistician | "We Do Everything With Heart" to Improve and Extend Lives| Clinical Trials | Analyzing Health…
Kathryn Hornburg

1 Peer Consultation

One of the most direct ways to get feedback on your statistical work is to consult with peers. Reach out to colleagues or classmates who have experience in statistics or the specific area of your study. They can offer fresh perspectives and may spot issues you've overlooked. When sharing your work, be clear about where you need input; whether it's the experimental design, the choice of statistical tests, or the interpretation of results. Remember to be open-minded and consider the critiques seriously, as they can significantly refine your analysis.

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Yogita Kolekar Thoke??

?Global Biostatistician | "We Do Everything With Heart" to Improve and Extend Lives| Clinical Trials | Analyzing Health Data for Evidence-Based Insights and Public Health Impact??|LinkedIn Top Voice
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I would recommend below steps: 1. Consult with super seniors or colleagues or mentors 2. Join statistical forums or communities online 3. Attend conferences, workshops, or seminars related to your field of study. 4. Submit your work for peer review and get feedback. It will help to improve your work and if needed any changes. 5. Seek consultation from SMEs, if you need to know more depth about your analysis. Remember, seeking feedback is an essential part of the scientific process. By actively seeking input from others, you can strengthen the validity and robustness of your statistical experimental design and analysis.

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Kathryn Hornburg
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Working from the academic sphere here, many institutions have a dedicated statistician you can reach out to for consultation or if not there could always be a statistics department to discuss with (many high level stats classes like case studies).

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Engaging peers for consultation enhances research quality. In refining statistical experimental designs, peer feedback is invaluable. Collaboration fosters diverse perspectives, enriching methodology. Communicate objectives clearly, encouraging honest critique. Consider multiple viewpoints, iterating for excellence. Gratitude for peers' contributions is paramount.

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Mere Oluwatosin

Research Technician at International Institute of Tropical Agriculture (IITA)
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To seek experts consultant I will try to recheck the hypothesis I will also check the objective of the projects. I join a statistical forum

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2 Expert Advice

If you're looking for more specialized feedback, seeking advice from an expert in the field can be invaluable. Experts can provide insights based on years of experience and deep knowledge of statistical methodologies. You might find these experts at your institution or by reaching out to authors of papers you've cited in your work. When presenting your design and analysis to an expert, ensure that your work is as polished as possible to make the most of their time and to encourage a constructive response.

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Expert consultation is your compass. Seek counsel from seasoned statisticians, refining methodologies for robustness. Clarify objectives and hypotheses, embracing diverse insights. Transparent communication fosters invaluable critique. Embrace feedback loops, iterating towards perfection. Express gratitude for shared wisdom, fostering collaboration.

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3 Online Forums

In today's digital age, online forums and communities dedicated to statistics offer a platform for feedback from a diverse audience. Websites like Stack Exchange or Reddit have active communities where you can post specific questions or request general advice on your experimental design and analysis. Be specific about what you need help with and be prepared for a wide range of feedback. Engaging with these communities requires a thick skin, as not all feedback will be delivered gently, but the diversity of opinions can be very enlightening.

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Online forums are your secret weapon. Engage with vibrant communities, tapping into diverse expertise. Share your design and analysis plan transparently, inviting constructive feedback. Embrace different perspectives, enriching your approach. Actively participate in discussions, refining methodologies iteratively. Express gratitude for invaluable insights, fostering a culture of collaboration.

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4 Workshops and Seminars

Attending workshops and seminars is an excellent way to receive feedback while also learning new skills. These events often provide opportunities for participants to present their work and receive critiques from both peers and experts. Look for events that are relevant to your field of study or that focus on statistical methods. When presenting at such gatherings, try to distill your work into key points to facilitate a clear and focused discussion.

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Sachin Nomula

"Data Science Enthusiast | 2X Microsoft Azure certified | NLP, Deep Learning & Machine Learning | Aspiring Deep Learning Engineer | Committed to Harnessing Data for Innovative Solutions"
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Attend workshops and seminars! Immerse yourself in dynamic learning environments, soaking up expert insights. Engage with peers, sharing and refining experimental designs collaboratively. Explore cutting-edge methodologies, embracing innovation. Actively participate in discussions, gaining diverse perspectives. Apply newfound knowledge iteratively, optimizing your approach. Express gratitude for the enriching experience, fostering a culture of continuous improvement

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5 Academic Journals

Submitting your work to an academic journal is a formal way to seek feedback. Peer reviewers will evaluate your experimental design and analysis rigorously. This process can be daunting, but it's also an opportunity to have your work scrutinized by professionals. Before submission, ensure that your paper follows the journal's guidelines and that your arguments are well-supported by your data and analysis. The feedback from this process, whether you are published or not, can be incredibly valuable for improving your work.

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6 Professional Consultants

Sometimes, the best option might be to hire a professional consultant who specializes in statistical analysis. A consultant can provide detailed, personalized feedback and guidance tailored to your specific project. While this option may incur a cost, the investment can pay off by significantly improving the quality of your work. Ensure that you clearly communicate your goals and concerns to the consultant so they can focus their expertise where it's most needed.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What do you do if your statistical experimental design and analysis needs feedback?

1

2

3

4

5

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7

1 Peer Consultation

2 Expert Advice

3 Online Forums

4 Workshops and Seminars

5 Academic Journals

6 Professional Consultants

7 Here’s what else to consider

Statistics

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What do you do if your statistical experimental design and analysis needs feedback?

1

2

3

4

5

6

7

1 Peer Consultation

2 Expert Advice

3 Online Forums

4 Workshops and Seminars

5 Academic Journals

6 Professional Consultants

7 Here’s what else to consider

Statistics

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