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Statistical mentoring at early training and career stages

Journal Article · · American Statistician

At Los Alamos National Laboratory (LANL), statistical scientists develop solutions for a variety of national security challenges through scientific excellence, typically as members of interdisciplinary teams. At LANL, mentoring is actively encouraged and practiced to develop statistical skills and positive career-building behaviors. Mentoring activities targeted at different career phases from student to junior staff are an important catalyst for both short and long term career development. This article discusses mentoring strategies for undergraduate and graduate students through internships as well as for postdoctoral research associates and junior staff. Topics addressed include project selection, progress, and outcome; intellectual and social activities that complement the student internship experience; key skills/knowledge not typically obtained in academic training; and the impact of such internships on students’ careers. Experiences and strategies from a number of successful mentorships are presented. Feedback from former mentees obtained via a questionnaire is incorporated. As a result, these responses address some of the benefits the respondents received from mentoring, helpful contributions and advice from their mentors, key skills learned, and how mentoring impacted their later careers.

Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
DOD; USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1312573
Report Number(s):
LA-UR-15-29115
Journal Information:
American Statistician, Journal Name: American Statistician; ISSN 0003-1305
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English

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Statistical Engineering—Forming the Foundations journal April 2012
Statistical Engineering—Roles for Statisticians and the Path Forward journal April 2012
Analysis of interval-censored data with random unknown end points: an application to soft error rate estimation: Soft Error Rate Estimation
  • Michalak, Sarah E.; Hamada, Michael S.; Hengartner, Nicolas W.
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journal January 2013
Statistical Research: Some Advice for Beginners journal May 2004

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