Name: Experiment Design
Start: 2018-04-26T18:30:00+01:00
End: 2018-04-26T20:00:00+01:00
Location: Facebook

Venue Capacity: 80 people

The capacity of the venue is about 80 people but because of no-shows, we have a policy of first-come, first-served on the night in the event of the room filling up. Managing no-shows is a problem all Meetups have to deal with and this is the fairest method we can think of. While we have never been in a position to turn anyone away for capacity reasons, it is always a possibility, so please arrive early to avoid disappointment on the night.

Experiments can help us move beyond simply observing behaviour, to understanding why behaviour occurs. This not only helps us to understand people better (people who are, for instance, customers, potential customers, clients, and so on) – but also to understand how to change their behaviour (not as creepy as it sounds).

A successful behavioural experiment is underpinned by careful design. The design of your experiment will be essential for establishing causality – and will ultimately determine the success of any resulting intervention.

In this presentation Freyja Quick (Lead for Research & Design on AIB’s Analytics Innovation team) will take a look at what makes an experiment a useful tool for developing interventions, discuss key design principles, and explore some common design pitfalls and how they can be avoided.

Mick Cooney

Richard Morrisroe

Giorgi Jvaridze

Dublin Data Science

Technology

Artificial Intelligence

Open Source

Data Management

Business Intelligence

Hadoop

Big Data

Machine Learning

Data Mining

NoSQL

Data Analytics

Data Visualization

Predictive Analytics

Data Science

Applied Statistics

Experiment Design

Facebook

Share

Dublin Data Science

Experiment Design

Dublin Data Science

Details

Members are also interested in