Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.

As part of the “Biostatistics and Evidence Appraisal for Radiation Oncologists”, an online seminar series is sponsored by the University of Cincinnati Department of Radiation Oncology and ROECSG (Radiation Oncology Education Collaborative Study Group https://voices.uchicago.edu/roecsg/), Dr. Clifton fuller presented exemplar data from the following publications of prospective trial performed under the auspices of the University of Texas MD Anderson Cancer Center (Trial No, 88-001):-Peters LJ, Goepfert H, Ang KK, Byers RM, Maor MH, Guillamondegui O, Morrison WH, Weber RS, Garden AS, Frankenthaler RA, et al. Evaluation of the dose for postoperative radiation therapy of head and neck cancer: first report of a prospective randomized trial. Int J Radiat Oncol Biol Phys. 1993 Apr 30;26(1):3-11. doi: 10.1016/0360-3016(93)90167-t. PMID: 8482629.-Rosenthal DI, Mohamed ASR, Garden AS, Morrison WH, El-Naggar AK, Kamal M, Weber RS, Fuller CD, Peters LJ. Final Report of a Prospective Randomized Trial to Evaluate the Dose-Response Relationship for Postoperative Radiation Therapy and Pathologic Risk Groups in Patients With Head and Neck Cancer. Int J Radiat Oncol Biol Phys. 2017 Aug 1;98(5):1002-1011. doi: 10.1016/j.ijrobp.2017.02.218. Epub 2017 Jul 10. PMID: 28721881; PMCID: PMC5518636.Data from these publications was anonymized (I.e. stripped of 45 CFR § 164.514- defined PHI identifiers); age values were “scrambled” in random order, such that they are not associated directly with the index patient case-data. The resultant dataset is presented as a .csv file for use for training and statistical instruction purposes.

Basic descriptive statistics.

Building strong quantitative skills prepares undergraduate biology students for successful careers in science and medicine. While math and statistics anxiety can negatively impact student learning within biology classrooms, instructors may reduce this anxiety by steadily building student competency in quantitative reasoning through instructional scaffolding, application-based approaches, and simple computer program interfaces. However, few statistical programs exist that meet all needs of an inclusive, inquiry-based laboratory course. These needs include an open-source program, a simple interface, little required background knowledge in statistics for student users, and customizability to minimize cognitive load, align with course learning outcomes, and create desirable difficulty. To address these needs, we used the Shiny package in R to develop a custom statistical analysis application. Our “BioStats” app provides students with scaffolded learning experiences in applied statistics that promotes student agency and is customizable by the instructor. It introduces students to the strengths of the R interface, while eliminating the need for complex coding in the R programming language. It also prioritizes practical implementation of statistical analyses over learning statistical theory. To our knowledge, this is the first statistics teaching tool where students are presented basic statistics initially, more complex analyses as they advance, and includes an option to learn R statistical coding. The BioStats app interface yields a simplified introduction to applied statistics that is adaptable to many biology laboratory courses.

Primary Image: Singing Junco. A sketch of a junco singing on a pine tree branch, created by the lead author of this paper.

A quick refresher course for those who have had statistical training in the past or a fast-paced introduction to basic statistics for beginners. Statistical measures such as percentages, averages, frequency and standard error are used widely. But how are they calculated, and exactly what do they tell us? This one day workshop will help participants develop an appreciation of the potential of statistics and a critical eye of when and how they should or shouldn't be used.

As the paired-end reads from the experiment SRX006998 are of different length, we include in this dataset only reads from one end.

Descriptive statistics for the basic performance measures.

CH: People Using At Least Basic Sanitation Services: % of Population data was reported at 99.891 % in 2015. This records a decrease from the previous number of 99.892 % for 2014. CH: People Using At Least Basic Sanitation Services: % of Population data is updated yearly, averaging 99.896 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 99.900 % in 2000 and a record low of 99.891 % in 2015. CH: People Using At Least Basic Sanitation Services: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank: Health Statistics. The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

Variable selection, basic meaning and descriptive statistics.

This dataset is about book subjects. It has 1 row and is filtered where the books is Basic statistics in behavioural research. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about book series. It has 1 row and is filtered where the books is Basic statistics with business applications. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The cardinality of an MCS is defined as the number of reactions present in the MCS. Its signature corresponds to the list of modules (cf. Table 3) that are targeted by the MCS. The table lists the number of distinct signatures obtained for each output reaction, as well as the total number of targeted modules.

Biology students’ understanding of statistics is incomplete due to poor integration of these two disciplines. In some cases, students fail to learn statistics at the undergraduate level due to poor student interest and cursory teaching of concepts, highlighting a need for new and unique approaches to the teaching of statistics in the undergraduate biology curriculum. The most effective method of teaching statistics is to provide opportunities for students to apply concepts, not just learn facts. Opportunities to learn statistics also need to be prevalent throughout a student’s education to reinforce learning. The purpose of developing and implementing curriculum that integrates a topic in biology with an emphasis on statistical analysis was to improve students’ quantitative thinking skills. Our lesson focuses on the change in the richness of native species for a specified area with the aid of iNaturalist and the capacity for analysis afforded by Google Sheets. We emphasized the skills of data entry, storage, organization, curation and analysis. Students then had to report their findings, as well as discuss biases and other confounding factors. Pre- and post-lesson assessment revealed students’ quantitative thinking skills, as measured by a paired-samples t test, improved. At the end of the lesson, students had an increased understanding of basic statistical concepts, such as bias in research and making data-based claims, within the framework of biology.

Primary Image: Website screenshot of an iNaturalist observation (Clasping Milkweed – Asclepias amplexicalis). This image is an example of a data entry on iNaturalist. The data students export from iNaturalist is made up of hundreds, or even thousands, of observations like this one. This image is licensed under Creative Commons Attribution - Share Alike 4.0 International license. Source: Observation by cassi saari, 2014.

Question Paper Solutions of chapter Basic Statistics of Mathematics - II A, 2nd Semester , Bachelor of Technology

PL: People Using At Least Basic Sanitation Services: % of Population data was reported at 98.133 % in 2015. This records an increase from the previous number of 97.378 % for 2014. PL: People Using At Least Basic Sanitation Services: % of Population data is updated yearly, averaging 92.524 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 98.133 % in 2015 and a record low of 87.012 % in 2000. PL: People Using At Least Basic Sanitation Services: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank: Health Statistics. The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

Question Paper Solutions of chapter Descriptive Statistics of Basic Data Science, 3rd Semester , Master of Computer Applications (2 Years)

TM: People with Basic Handwashing Facilities Including Soap and Water: Rural: % of Rural Population data was reported at 96.675 % in 2015. This records an increase from the previous number of 94.269 % for 2014. TM: People with Basic Handwashing Facilities Including Soap and Water: Rural: % of Rural Population data is updated yearly, averaging 78.631 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 96.675 % in 2015 and a record low of 60.588 % in 2000. TM: People with Basic Handwashing Facilities Including Soap and Water: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkmenistan – Table TM.World Bank: Health Statistics. The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; ;

This dataset is about book subjects. It has 7 rows and is filtered where the books is Basic statistics for the behavioral sciences. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

PRISMA Checklist. (DOC)

Basic statistics of the recorded click source levels (SLs) and comparison between target-present and target-absent conditions (T-test for independent samples, i.e., variables were treated as independent samples).