# Determining the Cause of Performance Problems

Analyzing Performance Results

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**2**

Create a Performance report using only the interval specified by these two time

stamps.

**3**

Graph the Performance report to verify that the distribution has flattened.

**4**

Run the Performance, Compare Performance, and Command Usage reports to

examine the summary statistics for this measurement interval.

**Determining the Cause of Performance Problems**

The third level of analysis helps you understand the causes and significance of

performance problems.

**Analyzing Results Statistically**

This detailed analysis takes the low-level data and uses statistical testing to help draw

useful conclusions. Although this analysis provides objective and quantitative

criteria, it is more time consuming than first- and second-level analysis and requires a

basic understanding of statistics.

When you analyze your data at this level, you use the concept of *statistical significance*

to help discern whether differences in response time are real or are due to some

random event associated with the test data collection. On a fundamental level,

randomness is associated with any event. Statistical testing determines whether there

is a systematic difference that cannot be explained by random events. If the difference

was not caused by randomness, the difference is statistically significant.

To perform a third-level analysis, run the Performance and Response vs. Time reports.

Some of the measurements to consider during third-level analysis are:

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**Minimum**

The lowest response time.

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**Maximum**

The highest response time.

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**Mean**

The average response time. This average is computed by adding all of the

response time values together and then dividing that total by the number of

response time values.

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**Median**

The midpoint of the data. Half of the response time values are less than

this point and half of them are greater than this point.

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**Standard Deviation**

How tightly the data is grouped around the mean.

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**Percentiles**

The percentages of response times above or below a certain point.

The 90th percentile is often measured.

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**Outlier**

A value that is much higher or lower than the others in the data.