This visualization activity combines student data collection with the use of an applet to enhance the understanding of the distributions of mean square treatment (MST), mean square error (MSE) as well as their ratio, an F-distribution. Students will see theoretical distributions of the mean square treatment, mean square error and their ratio and how they compare to the histograms generated by the simulated data.

In this publication we describe the Project completed as part of a standard Statistics course at Middlesex Community College, Beford-Lowell, MA.

Using actual wildlife injury data from a local wildlife rescue center, students learn what animal species have been injured and the causes of injury. Students use spreadsheet software to sort, organize, and evaluate their findings for recommendations to reduce human-caused injury to wildlife. Students prepare and present a summary of their findings and recommendations to the local Audubon Society, The Humane Society, neighborhood associations, and other interested groups. At the end of each public presentation, students gather public reaction to the data and collect ideas on how to reduce injury to wildlife. These recommendations are compiled into a newsletter and wiki for dissemination to a wider audience.

This unit plan was originally developed by the Intel® Teach program as an exemplary unit plan demonstrating some of the best attributes of teaching with technology.

This video is the first in a series of videos related to the basics of power analyses. All materials shown in the video, as well as content from the other videos in the power analysis series can be found here: https://osf.io/a4xhr/

In this problem-based learning module students will connect these standards to their personal life by completing a random sample from their environment in the area of careers to investigate to see if their own career is environmentally challenged.  Students will work independently as well as with a partner. Students will also complete a reflection in the form of a final product to make an inference and draw a conclusion about the population of their area in relation to careers.  The final product will be presented to their peers and teachers, but also can be exhibited to their families.    This blended module includes teacher-led instruction, student-led stations, partner comparisons and technology integrated investigations.

Think you're good at guessing stats? Guess again. Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited, says data visualization expert Alan Smith. In this delightful talk, Smith explores the mismatch between what we know and what we think we know.

Background The widespread reluctance to share published research data is often hypothesized to be due to the authors' fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically. Methods and Findings We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance. Conclusions Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.

In this lesson, students will learn to find and use z-scores to compare data. Through videos and interactive questions with immediate feedback they can practice the basics of z-score usage.

The widespread use of ‘statistical significance’ as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (according to the American Statistical Association). We review why degrading p-values into ‘significant’ and ‘nonsignificant’ contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p-values at face value, but mistrust results with larger p-values. In either case, p-values tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance (p ≤ 0.05) is hardly replicable: at a good statistical power of 80%, two studies will be ‘conflicting’, meaning that one is significant and the other is not, in one third of the cases if there is a true effect. A replication can therefore not be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgment based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to selective reporting and to publication bias against nonsignificant findings. Data dredging, p-hacking, and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p-values should be interpreted as graded measures of the strength of evidence against the null hypothesis. Also larger p-values offer some evidence against the null hypothesis, and they cannot be interpreted as supporting the null hypothesis, falsely concluding that ‘there is no effect’. Information on possible true effect sizes that are compatible with the data must be obtained from the point estimate, e.g., from a sample average, and from the interval estimate, such as a confidence interval. We review how confusion about interpretation of larger p-values can be traced back to historical disputes among the founders of modern statistics. We further discuss potential arguments against removing significance thresholds, for example that decision rules should rather be more stringent, that sample sizes could decrease, or that p-values should better be completely abandoned. We conclude that whatever method of statistical inference we use, dichotomous threshold thinking must give way to non-automated informed judgment.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Studying disease- and treatment-associated changes in bacterial abundance can clarify microbiome-related pathogenic and therapeutic mechanisms. However, the existing tests are not ideal for microbiome data, as they often have high positive rates or lack of power. Most of them also perform poorly when heterogeneous effects are present, and some are ill-suited for more than two groups or covariate adjustment. As an alternative, researchers recently developed a non-parametric zero-inflated quantile approach (ZINQ), which uses a two-part model. In part 1, logistic regression determines whether the variable of interest is associated with taxon presence/absence. In part 2, on selected quantile levels, quantile regression on the non-zero observations tests whether the variable is associated with altered taxon abundance. Finally, the marginal p-values are combined, with a significant aggregate p-value indicating differential abundance..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.