Communicate Many More Means

Business Intelligence Dashboards present frequencies, percentages and averages in one convenient location.   In this example, there are five average “Wait Time” scores compared for meaningful differences.  Each average Wait Time score represents a single week of calls to a Government Department. Total number of calls, or “n” appears in the Total Calls column.  It is important to know “n”, in this case call volumes, when interpreting averages.  Calculated Wait Time averages appear in the Average Wait Time  column (second table). There is no need to guess from the bar chart what the exact values are.

Many More Means

This example is from a Government Website (http://www.tpsgc-pwgsc.gc.ca/pension/qenrcm-hdwdtm-eng.html).  It’s a great resource with lots of interesting analytics on key performance indicators.

General Questions From an Operations Manager:

Is the Wait Time average downward trend a good thing?  Are Wait Time averages really different for each week?  Is there a significant improvement in Wait Times over the five-week period?  What does the increase in the last week suggest?  Should something be done to make improvements in our service delivery since it looks like Wait Times are going back up?  Do we hire or fire call centre staff?

Research Questions From the Analytics Manager:

Are the differences in the average Wait Times statistically meaningful?  If they are, what month(s) show the greatest improvement?  Depending on the statistical results, are the changes in average Wait Time practically meaningful?

Hypothesis From the Statistician:

Ho:  There is No statistical difference in average Wait Time for the five weeks.

Ha:  There IS a statistical difference in average Wait Time for the five weeks. 

Statistical Test:

The One-way Analysis of Variance (ANOVA) is the appropriate statistical test to use when comparing the differences between three or more averages.

Results & Interpretation:

In this example, the ANOVA will determine if the observed differences in average Wait Time is statistically different.  The chart suggests a dramatic downward trend with large differences.  The ANOVA test will help managers make a practical interpretation of the apparent trend.  For example, if the downward trend is statistically significant, managers may check customer satisfaction to confirm if a decrease in Wait Time by 41 seconds really improves service? If the trend is statistically significant, managers may also want to reward staff for their excellent performance.

More about the ANOVA test: http://www.csse.monash.edu.au/~smarkham/resources/anova.htm

ANOVA test in SPSS: https://statistics.laerd.com/spss-tutorials/one-way-anova-using-spss-statistics.php

Communicating t-test Results

General Question:  Your employer says, I think men take more sick days then women in this company, and we need to do something about it.  Let me know what the stats are.

Research Question:  Is there a significant difference between the average number of sick days taken by men and women in the past 12 months?

Hypotheses:  There IS NO significant difference between average number of sick days for men and women during the past 12 months (Null Hypothesis).  There IS a significant difference between average number of sick days for men and women during the past 12 months (Alternative Hypothesis).

Analysis Plan: Because the research question is about differences between two averages, Male and Female average sick days, the Independent groups t-test is the best statistic to use.

For more on how to pick the best statistical test please visit:

http://www.ats.ucla.edu/stat/mult_pkg/whatstat/

Calculate Statistic: Calculate the average number of sick days for male and female employees using IBM SPSS.  Use the Independent Groups t-test to determine if the difference between the average Male and Female sick days is significant.

Compute Probability:  When the results of the t-test show that the null hypothesis has less than a 1% chance of being right, we reject it and suggest the alternative hypothesis is worth considering.

For more on the concept of the p-value please visit:

http://askville.amazon.com/explain-concept-p-value-simple-English/AnswerViewer.do?requestId=6438921

Communicate Results like a Statistician:

Mr. Boss, we used an Independent groups t-test to determine if there is a significant difference in the average number of sick days taken by male and female employees in the past 12 months. You will be happy to know that the results were: t=-3.7341(198), p<.01 and the results were significant.  Therefore we rejected the null hypothesis. 

Therefore there IS a significant difference between the average sick days for men (50.1) and the average sick days of women (54.99) in the past 12 months.  

Women in this company took more sick days than men in the past 12 months.

IBM SPSS Output for Independent Groups t-test:

IBM SPSS t-test Output

IBM SPSS t-test Output

To learn more about the t-test used in this article please visit:

http://www.ats.ucla.edu/stat/spss/output/Spss_ttest.htm