The things that are changing in an experiment are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types.

Variables such as temperature, humidity, pressure, gravity, elevation, vibration, stress, strain, lighting, etc. can impact the measurement result. Some tests and calibrations are more sensitive to certain environmental factors than others.

Method comparison

1. Correlation coefficient. A correlation coefficient measures the association between two methods.
2. Scatter plot. A scatter plot shows the relationship between two methods.
3. Fit Y on X.
4. Linearity.
5. Residual plot.
6. Average bias.
7. Difference plot (Bland-Altman plot)
8. Fit differences.

When comparing two groups, you need to decide whether to use a paired test. When comparing three or more groups, the term paired is not apt and the term repeated measures is used instead. Use an unpaired test to compare groups when the individual values are not paired or matched with one another.

When comparing more than two sets of numerical data, a multiple group comparison test such as one-way analysis of variance (ANOVA) or Kruskal-Wallis test should be used first.

How to Compare Data Sets

1. Center. Graphically, the center of a distribution is the point where about half of the observations are on either side.
2. Spread. The spread of a distribution refers to the variability of the data.
3. Shape. The shape of a distribution is described by symmetry, skewness, number of peaks, etc.
4. Unusual features.

To compare two data models

1. Open one of the models you want to compare, then click Complete Compare on the Actions menu.
2. Click Load… to browse for the second *.
3. Use the options in the Complete Compare Wizard to set the compare level and filter by objects for either model.
4. Click Compare to start the compare process.

A comparison chart is a chart that draws a comparison between two or more items on different parameters. You can either compare two items such as in the example below. Or, you can use various parameters or comparison points to weigh up two or more items.

How to compare two means using Excel

1. Launch Microsoft Excel.
2. Type in a heading for your first (input) variable in cell A1. Don’t forget the units!
3. Type in a heading for your second (outcome) variable in cell B1.
4. Now enter your data down columns A and B.
5. Save your file.

A t-test tells you whether the difference between two sample means is “statistically significant” – not whether the two means are statistically different. A t-score with a p-value larger than 0.05 just states that the difference found is not “statistically significant”.

This suggests that the standard deviation is smaller in data set 2 than data set 1. When comparing distributions, it is better to use a measure of spread or dispersion (such as standard deviation or semi-interquartile range) in addition to a measure of central tendency (such as mean, median or mode).

One way to compare the two different size data sets is to divide the large set into an N number of equal size sets. The comparison can be based on absolute sum of of difference. THis will measure how many sets from the Nset are in close match with the single 4 sample set.

According to Keppel (1993), there is no good rule of thumb for how unequal the sample sizes need to be for heterogeneity of variance to be a problem. So if you have equal variances in your groups and unequal sample sizes, no problem. If you have unequal variances and equal sample sizes, no problem.

In business meetings, it’s common to compare groups of numbers by comparing their averages. But doing so often hides important information in the distribution of the numbers in those groups. There are a number of data visualizations that shine a light on this information.

Even though you can perform a t-test when the sample size is unequal between two groups, it is more efficient to have an equal sample size in two groups to increase the power of the t-test. Welch’s t-test is for unequal variance data.