Presenting on "Data peeking without p-hacking? A guide to Sequential Analysis"

Agenda:
2:30—3:0 pm CDT: Presentation “Data peeking without p-hacking? A guide to Sequential Analysis”
3:30—4:30 pm CDT: Q & A, prioritizing trainees

Daniël Lakens, PhD, associate professor in the human-technology interaction group at Eindhoven University of Technology, will speak at 2:30 pm CDT on Monday, March 21, 2022. This workshop is virtual and registration is required. Register for this workshop.

Abstract: When designing an experiment, it is essential to think about the sample size you need to collect. However, due to uncertainty in the effect size you will observe, it is often difficult to perform an a-priori power analysis. It would often be much more convenient to collect data in small batches and repeatedly analyze the data, were it not for the fact that this is known as a ‘p-hacking’, and inflates the Type 1 error rate. In this workshop we will discuss statistical techniques to sequentially analyze data in small batches, or even after every participant, without inflating error rates. Such approaches are efficient and flexible ways to collect data when the expected effect size is uncertain. We will focus on sequential analyses technique developed in bio-statistics. This workshop has a pragmatic, hand-on approach, and we will discuss how to incorporate feasibility constraints, stopping either after concluding the presence or the absence of a meaningful effect, and free software tools that exists to design and analyze sequential trials.

Presenting on "Data peeking without p-hacking? A guide to Sequential Analysis"

Agenda:
2:30—3:0 pm CDT: Presentation “Data peeking without p-hacking? A guide to Sequential Analysis”
3:30—4:30 pm CDT: Q & A, prioritizing trainees

Daniël Lakens, PhD, associate professor in the human-technology interaction group at Eindhoven University of Technology, will speak at 2:30 pm CDT on Monday, March 21, 2022. This workshop is virtual and registration is required. Register for this workshop.

Abstract: When designing an experiment, it is essential to think about the sample size you need to collect. However, due to uncertainty in the effect size you will observe, it is often difficult to perform an a-priori power analysis. It would often be much more convenient to collect data in small batches and repeatedly analyze the data, were it not for the fact that this is known as a ‘p-hacking’, and inflates the Type 1 error rate. In this workshop we will discuss statistical techniques to sequentially analyze data in small batches, or even after every participant, without inflating error rates. Such approaches are efficient and flexible ways to collect data when the expected effect size is uncertain. We will focus on sequential analyses technique developed in bio-statistics. This workshop has a pragmatic, hand-on approach, and we will discuss how to incorporate feasibility constraints, stopping either after concluding the presence or the absence of a meaningful effect, and free software tools that exists to design and analyze sequential trials.