During the third quarter of 2024, data breaches exposed more than 422 million records worldwide. Since the first quarter of 2020, the highest number of data records were exposed in the first quarter of 202, more than 818 million data sets. Data breaches remain among the biggest concerns of company leaders worldwide. The most common causes of sensitive information loss were operating system vulnerabilities on endpoint devices. Which industries see the most data breaches? Meanwhile, certain conditions make some industry sectors more prone to data breaches than others. According to the latest observations, the public administration experienced the highest number of data breaches between 2021 and 2022. The industry saw 495 reported data breach incidents with confirmed data loss. The second were financial institutions, with 421 data breach cases, followed by healthcare providers. Data breach cost Data breach incidents have various consequences, the most common impact being financial losses and business disruptions. As of 2023, the average data breach cost across businesses worldwide was 4.45 million U.S. dollars. Meanwhile, a leaked data record cost about 165 U.S. dollars. The United States saw the highest average breach cost globally, at 9.48 million U.S. dollars.

A survey conducted in May 2024 found that 56 percent of the respondents in the United States were not likely at all to trust a company that had experienced data breaching with their personal data. The same trend was reflected among different age groups, as the majority of them were not likely to trust companies post-data breach. A significant share of 76 percent of adults between the age of 45 and 54 were not likely to share their personal information with a company after a data breach. While approximately half of users aged between 25 and 44 were not at all likely to trust such companies in the future.

A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as "p-hacking," occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.

Number of p-values in each bin is the mean number based on 1,000 bootstraps of one p-value per Abstract, rounded to the nearest whole number. Disciplines (n = 12) for which we found fewer than 50 p-values below 0.05 in the Abstract were excluded.Tests for evidential value and p-hacking across disciplines, using p-values obtained from the Abstract.