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.

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.

Descriptive statistics for the basic performance measures.

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

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.

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.

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).

IL: People Using Basic Sanitation Services: Rural: % of Rural Population data was reported at 100.000 % in 2015. This stayed constant from the previous number of 100.000 % for 2014. IL: People Using Basic Sanitation Services: Rural: % of Rural Population data is updated yearly, averaging 100.000 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 100.000 % in 2015 and a record low of 100.000 % in 2015. IL: People Using Basic Sanitation Services: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.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;

One of the first steps in a reference interview is determining what is it the user really wants or needs. In many cases, the question comes down to the unit of analysis: what is it that is being investigated or researched? This presentation will take us through the concept of the unit of analysis so that we can improve our reference service — and make our lives easier as a result! Note: This presentation precedes Working with Complex Surveys: Canadian Travel Survey by Chuck Humphrey (14-Mar-2002).

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.

Variable selection, basic meaning and descriptive statistics.

BUSINESS PROBLEM-1 BACKGROUND: The Lending Club is a peer-to-peer lending site where members make loans to each other. The site makes anonymized data on loans and borrowers publicly available. BUSINESS PROBLEM: Using lending club loans data, the team would like to test below hypothesis on how different factors effecing each other (Hint: You may leverage hypothesis testing using statistical tests) a. Intrest rate is varied for different loan amounts (Less intrest charged for high loan amounts) b. Loan length is directly effecting intrest rate. c. Inrest rate varies for different purpose of loans d. There is relationship between FICO scores and Home Ownership. It means that, People with owning home will have high FICO scores. DATA AVAILABLE:  LoansData.csv The data have the following variables (with data type and explanation of meaning)  Amount.Requested - numeric. The amount (in dollars) requested in the loan application.  Amount.Funded.By.Investors - numeric. The amount (in dollars) loaned to the individual.  Interest.rate – character. The lending interest rate charged to the borrower.  Loan.length - character. The length of time (in months) of the loan.  Loan.Purpose – categorical variable. The purpose of the loan as stated by the applicant.  Debt.to.Income.Ratio – character. The % of consumer’s gross income going toward paying debts.  State - character. The abbreviation for the U.S. state of residence of the loan applicant.  Home.ownership - character. Indicates whether the applicant owns, rents, or has a mortgage.  Monthly.income - categorical. The monthly income of the applicant (in dollars).  FICO.range – categorical (expressed as a string label e.g. “650-655”). A range indicating the applicants FICO score.  Open.CREDIT.Lines - numeric. The number of open lines of credit at the time of application.  Revolving.CREDIT.Balance - numeric. The total amount outstanding all lines of credit.  Inquiries.in.the.Last.6.Months - numeric. Number of credit inquiries in the previous 6 months.  Employment.Length - character. Length of time employed at current job.

BUSINESS PROBLEM - 2 BACKGROUND: When an order is placed by a customer of a small manufacturing company, a price quote must be developed for that order. Because each order is unique, quotes must be established on an order-by-order basis by a pricing expert. The price quote process is laborintensive, as prices depend on many factors such as the part number, customer, geographic location, market, and order volume. The sales department manager is concerned that the pricing process is too complex, and that there might be too much variability in the quoted prices. An improvement team is tasked with studying and improving the pricing process. After interviewing experts to develop a better understanding of the current process, the team designed a study to determine if there is variability between pricing experts. That is, do different pricing experts provide different price quotes? Two randomly selected pricing experts, Mary and Barry, were asked to independently provide prices for twelve randomly selected orders. Each expert provided one price for each of the twelve orders. BUSINESS PROBLEM: We would like to assess if there is any difference in the average price quotes provided by Mary and Barry. DATA AVAILABLE:  Price_Quotes.csv The data set contains the order number, 1 through 12, and the price quotes by Mary and Barry for each order. Each row in the data set is the same order. Thus, Mary and Barry produced quotes for the same orders. BUSINESS PROBLEM-3: BACKGROUND: The New Life Residential Treatment Facility is a NGO that treatsteenagers who have shown signs of mental illness. It provides housing and supervision of teenagers who are making the transition from psychiatric hospitals back into the community. Because many of the teenagers were severely abused as children and have been involved with the juvenile justice system, behavioral problems are common at New Life. Employee pay is low and staff turnover (attrition) is high. A reengineering program wasinstituted at New Life with the goals of lowering behavioral problems of the kids and decreasing employee turnover rates. As a part of this effort, the following changes were made:  Employee shifts were shortened from 10 hours to 8 hours each day.  Employees were motivated to become more involved in patient treatments. This included encouraging staff to run varioustherapeutic treatment sessions and allowing staff to have more say in program changes.  The activities budget wasincreased.  A facility-wide performance evaluation system was putinto place that rewarded staff participation andinnovation.  Management and staff instituted a program designed to raise expectations about appropriate behavior from the kids. Thisincluded strict compliance with reporting of behavioral violations, insistence o...

Data source: SHARE waves 1, 2, and 4.

PRISMA Checklist. (DOC)

NFL-Statistics-Scrape

Here are the basic statistics, career statistics and game logs provided by the NFL on their website (http://www.nfl.com) for all players past and present.

Summary

The data was scraped using a Python code. The code can be located at Github: https://github.com/kendallgillies/NFL-Statistics-Scrape

Explanation of Data

1. The first main group of statistics is the basic statistics provided for each player. This data is stored in the CSV file titled Basic_Stats.csv along with the player’s name and URL identifier. If available the data pulled for each player is as follows:
   1. Number
   2. Position
   3. Current Team
   4. Height
   5. Weight
   6. Age
   7. Birthday
   8. Birth Place
   9. College Attended
   10. High School Attended
   11. High School Location
   12. Experience
2. The second main group of statistics gathered for each player are their career statistics. While each player has a main position they play, they will have statistics in other areas; therefore, the career statistics are divided into statistics types. The statistics are then stored in CSV files based on statistic type along with the player name, URL identifier and position (if available). The following are the career statistics types and accompanying CSV file names:
   1. Defensive Statistics – Career_Stats_Defensive.csv
   2. Field Goal Kickers - Career_Stats_Field_Goal_Kickers.csv
   3. Fumbles - Career_Stats_Fumbles.csv
   4. Kick Return - Career_Stats_Kick_Return.csv
   5. Kickoff - Career_Stats_Kickoff.csv
   6. Offensive Line - Career_Stats_Offensive_Line.csv
   7. Passing - Career_Stats_Passing.csv
   8. Punt Return - Career_Stats_Punt_Return.csv
   9. Punting - Career_Stats_Punting.csv
   10. Receiving - Career_Stats_Receiving.csv
   11. Rushing - Career_Stats_Rushing.csv
3. The final group of statistics is the game logs for each player. The game logs are stored by position and have the player name, URL identifier and position (if available). The following are the game log types and accompanying CSV file names:
   1. Quarterback – Game_Logs_Quarterback.csv
   2. Running back – Game_Logs_Runningback.csv
   3. Wide Receiver and Tight End – Game_Logs_Wide_Receiver_and_Tight_End.csv
   4. Offensive Line – Game_Logs_Offensive_Line.csv
   5. Defensive Lineman – Game_Logs_Defensive_Lineman.csv
   6. Kickers – Game_Logs_Kickers.csv
   7. Punters – Game_Logs_Punters.csv

Glossary

While most of the abbreviations used by the NFL have been translated in the table headers in the data files, there are still a couple of abbreviations used.

• FG: Field Goal
• TD: Touchdown
• Int: Interception

The students will practice identifying the appropriate basic statistical tests when given a scenario and learn how to run and interpret those statistical tests in R.

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.

This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.