It is often important to determine if a set of data is homogeneous before any statistical technique is applied to it. Homogeneous data are drawn from a single population. In other words, all outside processes that could potentially affect the data must remain constant for the complete time period of the sample. Inhomogeneities are caused when artificial changes affect the statistical properties of the observations through time. These changes may be abrupt or gradual, depending on the nature of the disturbance. Realistically, obtaining perfectly homogeneous data is almost impossible, as unavoidable changes in the area surrounding the observing station will often affect the data.
Interpreting climate data with unknown homogeneity:
Locate Dataset, Variable, and Station 


Select Temporal Domain 


Compute Yearly Mean Minimum Temperature 


View Yearly Mean Minimum Temperature Time Series 


Subtract Median From Dataset 


Analyze Homogeneity of Data 
Oliver, John E. Climatology: Selected Applications. p 7. There are 18 runs in the Sherbrooke data from 1920 to 1970. The total number of elements that make up the sample is 50 (each yearly mean minimum temperature constitutes one element). According to the table, at a .10 significance limit there should be at least 22 runs. We can therefore conclude, with 90% confidence, that this data is not homogeneous. Is this inhomogeneity caused by a largescale climatic change or by an inconsistancy in the area surrounding the observing station? To answer this question, we analyze the mean minimum temperature at another station only a few miles away. 
Locate Dataset and Variable 

Select Temporal Domain and Station 

Compute Yearly Mean Minimum Temperature 

View Yearly Mean Minimum Temperature Time Series 

Subtract Median From Dataset 

Analyze Homogeneity of Data 
