Hidden Publication Bias in Epidemiologic Secondary Data Analysis

We highlight a particular type of publication bias unique to secondary data analysis, and particularly common in epidemiologic research. We begin by setting a reminder of the scientific method of inquiry, and—by analogy with the movement for full transparency in clinical trials—present arguments for reporting all results of secondary data analysis. We then describe the ways in which data dredging—a subtle form a p-hacking—can lead to a distorted scientific literature; we highlight prior research that has empirically demonstrated this. We conclude by arguing that in order to combat this bias, epidemiologists should move toward preregistering analyses, and epidemiologic journals should encourage this through the implementation of Registered Reports. Finally, we respond to some common criticisms of preregistration.

Quantifying the data-dredging bias in structural break tests

Structural break tests are often applied as a pre-step to ensure the validity of subsequent statistical analyses. Without any a priori knowledge of the type of breaks to expect, eye-balling the data can indicate changes in some parameter, e.g., the mean. This, however, can distort the result of a structural break test for that parameter, because the data themselves suggested the hypothesis. In this paper, we formalize the eye-balling procedure and theoretically derive the implied size distortion of the structural break test. We also show that eye-balling a stretch of historical data for possible changes in a parameter does not invalidate the subsequent procedure that monitors for structural change in new incoming observations. An empirical application to Bitcoin returns shows that taking into account the data-dredging bias, which is incurred by looking at the data, can lead to different test decisions.

بحوث العلوم النفسية: الاستدلال الإحصائي والأزمة في قابلية التكرار

تتراكم المعرفة نتيجة لتتابع البحوث وتطورها، ويعتبر البحث العلمي أداة ووسيلة موضوعية للكشف عن الحقيقة العلمية، والعلم يقوم بالتصحيح الذاتي ليصحح مساره. تواجه بحوث العلوم النفسية قضايا منهجية مرتبطة بالاستدلال الإحصائي وبالتحديد سوء فهم واستخدام الدلالة الإحصائية أو القيم الاحتمالية (p-values)، وعدم قابلية النتائج للتكـرار. يقوم الباحثون بعملية تجريف البيانات (Data-dredging) لإيجاد نتائج ذات دلالة إحصائية تبرر النشر وذلك نتيجة للتنافس العالي في البيئة الأكاديميـة. تقدم هذه الورقة جهود العلماء والباحثين في علم النفس والإحصاءتجاه هذه القضايا، بما فيها بيان أصدرته الجمعية الإحصائية الأمريكية (ASA) American Statistical Association عن الدلالة الإحصائية والقيم الاحتمالية، والإطار العلمي المفتوح (OSF) Open Science Framework، ومشروع تعاوني قام به التعاون العلمي المفتوح بحيث تم تكرار 100 دراسة تجريبية وارتباطية للحصول على تقدير مبدئي في قابلية نتائج البحوث النفسية للتكرار. كما تقدم الورقة عدد من الحلول المقترحـة. يؤمل أن تحفـز هذه الورقة النقاش في وسط الباحثين وأن تفتح أفق جديدة للبحوث النفسيــة.

Hydrodynamic changes impacted by the waterway capital dredging in Cikarang Bekasi Laut channel, West Java, Indonesia

This paper presents one-dimensional numerical modeling using MIKE 11 to simulate the impact of capital dredging on the hydrodynamics of the Cikarang Bekasi Laut (CBL) channel flow. The CBL channel is located in Bekasi Regency, West Java Province, Indonesia. The river discharges upstream, and tidal fluctuations at the sea boundary were the governing parameters of the hydrodynamic model. Data such as river centerline, cross-sections, tidal elevation, and river discharges were compiled to construct the model. The instantaneous record of water level and river discharge data were used as model validation. The model results give decent validation when compared to water level and river discharge field data. Dredging on the canal is planned to be carried out across 19 km from the estuary to the upstream to allow large vessel navigation. The modeling results show that during the wet season, dredging affects the water level and river flow up to 25 km upstream, while during the dry season, dredging affects the hydrodynamics only up to 20 km upstream. It can be concluded that the canal dredging does not have a significant impact in terms of surface water elevation in the canal upstream. The critical finding is that the bed shear stress is significantly increased upstream of the dredging plan at kilometer 19, showing that there is potential riverbed erosion threat in the area. It is recommended to conduct a sedimentation study to predict the impact of sedimentation change from the dredging.

AN EMPIRICAL ANALYSIS ON DATA MINING SYSTEM AND ITS TECHNIQUES .

Data mining is an extraction of knowledge discovery from huge amount of data which is previously unknown and potentially useful for analytical processing and decision making. The other acronyms of data mining are such as Data archeology, Data dredging, Information harvesting and Business Intelligence. The various data mining techniques are used to find the hidden interestingness or new patter to store the data. These techniques and approaches of data mining can efficiently build the new environment for analyzing and predictions. This paper highlights data mining process and its various techniques to find the interestingness. Finally, concluded with its limitations. The objective of the paper is opens new horizons for researchers of forthcoming generations.

Military Database Security via Justapose Database Shuffling

This paper aims for Database security which expresses the need for preventing data leak from military databases which often leads to sensitive data leaks which bring the defence machinery of the nation at peril. Cyber-Attacks and Large-Scale Phishing from neighbouring nations have brought the immediate need of securing the database which concerns the confidentiality of the defence proceedings and hence the paper proposes some techniques and a unique project proposal to overcome the drawbacks of the existing technologies which can henceforth be secured to find a secure environment for the data. Lack of awareness regarding the gravity of the confidential data and what harm it might bring upon the defence set-up can lead to essential data being compromised and hence our paper proposes a novel solution in Database security by employing RSM method which invokes data dredging and mining.