By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Careful with the S-word
Share
Notification Show More
Latest News
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security
ai in software development
3 AI-Based Strategies to Develop Software in Uncertain Times
Software
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Careful with the S-word
Predictive Analytics

Careful with the S-word

DavidMSmith
Last updated: 2009/05/05 at 5:29 PM
DavidMSmith
5 Min Read
SHARE

Market researcher Tom Ewing offers some advice that applies equally well to statisticians — be careful when you use the word “significant” in its technical sense. Depending on the audience, it could lead to misunderstandings:

Non-researchers tend to misread “significant” as “important” or simply “big”. Which isn’t the case – it can be trivial or small, it’s just unlikely to be fluke or coincidence.

Researchers tend to read “significant” as “interesting”. Which isn’t the case either – even big results can be utterly banal, especially if they simply confirm something you could have guessed, or if they repeat information you already have.

It’s good advice in general, but with regard to the latter point we are given the following example:

Suppose we give 1,000 people an IQ test, and we ask if there is a significant difference between male and female scores. The mean score for males is 98 and the mean score for females is 100. We use an independent groups t-test and find that the difference is significant at the .001 level. The big question is, “So what?”. The difference between 98 and 100 on an IQ test is a very small difference… so small, in fact, that its not even important.

More Read

The Trouble with Big Data

Why Data Sampling Leads to Bad Decisions

Then…

Market researcher Tom Ewing offers some advice that applies equally well to statisticians — be careful when you use the word “significant” in its technical sense. Depending on the audience, it could lead to misunderstandings:

Non-researchers tend to misread “significant” as “important” or simply “big”. Which isn’t the case – it can be trivial or small, it’s just unlikely to be fluke or coincidence.

Researchers tend to read “significant” as “interesting”. Which isn’t the case either – even big results can be utterly banal, especially if they simply confirm something you could have guessed, or if they repeat information you already have.

It’s good advice in general, but with regard to the latter point we are given the following example:

Suppose we give 1,000 people an IQ test, and we ask if there is a significant difference between male and female scores. The mean score for males is 98 and the mean score for females is 100. We use an independent groups t-test and find that the difference is significant at the .001 level. The big question is, “So what?”. The difference between 98 and 100 on an IQ test is a very small difference… so small, in fact, that its not even important.

Then why did the t-statistic come out significant? Because there was a large sample size. When you have a large sample size, very small differences will be detected as significant. This means that you are very sure that the difference is real (i.e., it didn’t happen by fluke). It doesn’t mean that the difference is large or important. If we had only given the IQ test to 25 people instead of 1,000, the two-point difference between males and females would not have been significant.

Personally, I’m not so sure I’d dismiss that significant 2-point difference so lightly. 2 points may not be a meaningful difference in terms of IQ tests, but I’m immediately led to wonder why a significant difference was observed at all. Was there a problem with the sampling, that led to the men and women in the test being different in some way? Was there some kind of problem with the test, that favored women over men? If you get a significant result you don’t expect, it’s well worth investigating why — you may find a surprising, and dare I say, significant, problem with the way the experiment was conducted.

Blackbeard Blog: The Significance Problem (via @russhmeyer)

TAGGED: sampling
DavidMSmith May 5, 2009
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

The Trouble with Big Data

6 Min Read

Why Data Sampling Leads to Bad Decisions

3 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?