I'm doing a study about the height:radius ratios of limpets on exposed and sheltered shores (The ratio is calculated by dividing height by radius). There is a theoretical 'optimum' ratio which is 1.06. I have 2 samples: 219 limpets from an exposed shore and 299 limpets from a sheltered shore. I calculated the height:radius ratio of all the limpets sampled.

I want to statistically test if my observed ratios of the limpets are significantly different to the expected ratios of 1.06. I want to do this for the sheltered samples and then exposed samples separately to see if they have different results.

Here are my first 5 data for sheltered limpets...

observed ratios:

1.35

1.64

1.27

1.18

1.63

expected ratios:

1.06

1.06

1.06

1.06

1.06

Should I use chi squared goodness of fit test? I'm not sure because I thought the variables had to be categorical in order to do a chi sqaured test, but isn't ratio a continuous variable? would it be better to use a Mann Whitney U test? I have to use a non-parametric test because the data are not normally distributed. Any help is appreciated, thanks x

I want to statistically test if my observed ratios of the limpets are significantly different to the expected ratios of 1.06. I want to do this for the sheltered samples and then exposed samples separately to see if they have different results.

Here are my first 5 data for sheltered limpets...

observed ratios:

1.35

1.64

1.27

1.18

1.63

expected ratios:

1.06

1.06

1.06

1.06

1.06

Should I use chi squared goodness of fit test? I'm not sure because I thought the variables had to be categorical in order to do a chi sqaured test, but isn't ratio a continuous variable? would it be better to use a Mann Whitney U test? I have to use a non-parametric test because the data are not normally distributed. Any help is appreciated, thanks x

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