1. ## Parametric vs. Nonparametric.

Suppose a researcher estimated a parameter in parametric way with correct distributional assumption.

Another researcher estimated the same parameter in non-parametric way.

Will there any difference of accuracy (bias) in estimation in these two situations?

2. ## Re: Parametric vs. Nonparametric.

In general, nonparametric tests tend to be lower in power and have wider confidence intervals. Regarding bias, that will depend on the specific nonparametric test as well as your null hypothesis. Some nonparametric tests are based on the median vs. the mean. Some are based on the shape vs. the mean, etc. In those situations, there could be a potential bias relative to the mean.

3. ## Re: Parametric vs. Nonparametric.

I would imagine that if both methods met assumptions and were examining the same parameter construct, they would tend to generally merge given a large enough sample size. Though as Miner mentions it all depends on which statistical tests you are using.

4. ## Re: Parametric vs. Nonparametric.

Originally Posted by Cynderella
Another researcher estimated the same parameter in non-parametric way.
I believe that I understand that it is possible to estimate a parameter with parametric methods.

But I don't understand how it is possible to estimate a parameter in a non-parametric way, i.e. without assuming the existence of a parameter.

5. ## Re: Parametric vs. Nonparametric.

Originally Posted by GretaGarbo
I believe that I understand that it is possible to estimate a parameter with parametric methods.

But I don't understand how it is possible to estimate a parameter in a non-parametric way, i.e. without assuming the existence of a parameter.
Yepp, one difference might be that the parametric method will estimate a mean. The non-parametric method most probably the median . If the distribution is not symmetrical there will be a difference.

regards

6. ## Re: Parametric vs. Nonparametric.

Originally Posted by rogojel
Yepp, one difference might be that the parametric method will estimate a mean. The non-parametric method most probably the median . If the distribution is not symmetrical there will be a difference.
But isn't the median a parameter?

You can get the median from minimizing the absolute deviation (in contrast to least squares to get the mean). But isn't that a parametric method that estimates the parameter theta by minimizing Sum|y-theta| ?

7. ## Re: Parametric vs. Nonparametric.

Greta, I agree with the issues you are positing and have thought about them myself in the past. Yes, to me they are both parameters given the general definition of what parameters are. Though, it comes down to the distribution assumptions not being there in non-parameter approaches. So maybe we think the term parametric and non-parametric deal with the estimate, but it is actually dealing with the sampling distribution, etc.

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