Chi-Square Accustomator

Using a Mendelian simple dominant example

Character	Dominant	Recessive	Total
Observation
Ratio

Look at the Observations, and consider how close the Observed ratio is to the Expected 3:1 ratio. Is this a good fit? Is it a poor fit. The "Goodness of Fit" can be calculated by the Chi-Square method.

Result

Tally: > 3.841 > 6.63 Samples

Interpreting the Calculated Chi-Square Value
How good a fit is shown by a calculated Chi-square value of this size? The closer the calculated value is to 0, the better the fit is between the Observed and Expected. If the calculated value for two categories is >3.841 then there is a statistically "significant" difference between the Observed and the Expected. If the value is >6.63, the difference is "highly significant."
Yet such large differences do occur by chance now and then, even when the observations result from a true 3:1 ratio (as is the case here.) How often does that occur? The Tally shows the experience in this example.

Record of Tallies
Each set of samples is recorded in the text area below. You can review these at any time, and you can cut and past this information into your own records if you want to keep it.

Please send comments to hes@ncsu.edu --henry schaffer

Chi-Square Accustomator

Using a Mendelian simple dominant example

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