Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear. To address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction. We collected hypotheses from open-ended interviews and a literature review, followed by a validation survey. We received 395 responses from practitioners. Some of our key findings include: 1) Over 90% of respondents are willing to adopt defect prediction techniques. 2) There exists a disconnect between practitioners' perceptions and well supported research evidence regarding defect density distribution and the relationship between file size and defectiveness. 3) 7.2% of the respondents reveal an inconsistency between their behavior and perception regarding defect prediction. 4) Defect prediction at the feature level is the most preferred level of granularity by practitioners. 5) During bug fixing, more than 40% of the respondents acknowledged that they would make a "work-around" fix rather than correct the actual error-causing code. Based on our findings, we highlight future research directions and provide recommendations for practitioners.