- Blogs
- Discovery Lean Six Sigma
- What Is a P-Value?

**What Is a P-Value?**

Statistics is not an easy field, and anyone who’s had to analyze large volumes of data can quickly tell you the same. The problem often boils down to the fact that you simply can’t trust some discrepancies in the data, and you have to know where those differences are coming from, and how to interpret them properly.

Today, we have various tools that can help us more quickly identify when a given data set differs from another. The p-value is among the most important statistical terms within Six Sigma, and it’s critical that everyone using statistical analysis fully understands what it means.

**Meaning of the P-value**

The p-value is a number between zero and 1. It represents the probability that the groups within your data set came from the same distribution or behave similar to each other.

For example, if you are analyzing the time to commute to work, and you take 2 different routes to work, we can use p-values to determine if one route is statistically different than the other route. We always start out by assuming both routes are not different from one another. Then we calculate the average and standard deviations of both routes, and use those results to calculate the P-value.

If the p-value is closer to 1, then we would conclude that the routes are very similar to each other. If the p-value is closer to zero, then we would conclude that the routes are different statistically.

Since nothing in statistics is purely 100% guaranteed, the p-value represents the chance that we are incorrect, if we conclude the routes are different, when in fact, that only occurred in our sample (but not in reality). This could be due to random chance in how we collected our data. The closer the p-value to zero, the less chance that we will have made a mistake.

**Typical p-value ranges**

P-values are most interesting around the 0.05 limit, as this is often seen as the “cutoff” point for valid assumptions. Beneath a value of 0.05, we can make the claim that the risk of making an error is less than 5%, so we conclude that routes are different from one another.

On the other hand, p-values above 0.05 make it difficult for us to conclude that the routes are different, so we state that there is not enough data or evidence to tell them apart.

It’s interesting to note that a p-value that’s almost exactly 0.05 is a unique case, and it’s hard to draw any conclusions from it as the results can easily swing in both directions. In that situation, collecting more data is a good suggestion, to see if the additional results move the p-value closer to 0 or to 1. The other option is to take on slightly more risk of being wrong, if you conclude that the routes are different.

**Do I need to memorize these values?**

It’s important for anyone working closely with statistics to be able to interpret p-values, both when analyzing their own results, as well as when looking at a data set produced by someone else. In the very least, the 0.05 cutoff point should be memorized, and statisticians have come up with various sayings that can help with that.

The most popular one is:

“If P is low, H0 must go!”

This means that if the p-value is low (less than 0.05), then H0 (the null hypothesis that the groups are equal to each other) must “go”. “Go” would mean that we reject the null hypothesis (H0) and conclude that there is a statistical difference between the groups. Make sense?

**Conclusion**

Statistics can be very powerful, but can also be scary for many, especially if it isn’t something they use often.

Understanding the importance of the p-value as described in this article is a valuable skill for anyone involved in process improvement and especially Six Sigma. When p-values become commonplace in the work environment, you will know that your culture has made a tremendous shift in thinking!

**Do you know any other ways to remember p-values compared to hypothesis tests?** Add your comments below…

The post What Is a P-Value? appeared first on Shmula.

Original: http://www.shmula.com/what-is-a-p-value/23347/

By: Shmula Contributor

Posted: June 16, 2017, 5:42 am

Dummy user for scooping articles

I'm a dummy user created for scooping great articles in the network for the community.

- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- June 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- December 2016
- November 2016
- October 2016
- September 2016
- August 2016
- July 2016
- June 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- December 2015
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- August 2014
- July 2014
- June 2014
- April 2014
- March 2014
- February 2014
- January 2014
- December 2013
- November 2013
- October 2013
- September 2013
- August 2013
- July 2013
- June 2013
- May 2013
- April 2013
- March 2013
- February 2013
- January 2013
- November 2012
- October 2012
- September 2012
- August 2012
- July 2012
- June 2012
- May 2012
- March 2012
- February 2012
- November 2011
- October 2011
- September 2011
- August 2011
- July 2011
- June 2011
- May 2011
- April 2011
- February 2011
- January 2011
- December 2010
- November 2010
- October 2010
- September 2010
- August 2010
- July 2010
- June 2010
- April 2010
- March 2010
- February 2010
- December 2009
- November 2009
- August 2009
- June 2009
- March 2009
- November 2008
- October 2008
- July 2008
- May 2008
- April 2008
- March 2008
- February 2008
- June 2007
- February 2007
- August 2005

innovation, Leadership, Articles, big data, innovation excellence, Blogartikel, data management, Data Education, Education Resources For Use & Management of Data, & Education, lean manufacturing, Data Daily | Data News, lean, Quality Insider Article, Twitter Ed, Six Sigma, Business, Digitalisierung, systems thinking, Management Article, lean six sigma, Management, Big Data News, strategy, Lean Management, Gastbeiträge, Smart Data News, kaizen, The Latest, Problem solving, Interviews, Operations Article, continuous improvement, Soft Skills, Change, marketing, systems view of the world, Uncategorized, Organization, statistics, Theory of Constraints, Immobilien, Personal, Culture, quality, Banken, technology, Videos, agile, Sekretariat & Assistenz