Background

As I understand it (as a novice), parametric tests don't use your actual data as input but rather parameters extracted from your data which describe a distribution. The tests use the distributions which are modelled on your data.

Many researchers who have a limited understanding of the technical details of statistics learn about the normal distribution and that's about it. From what I have read, there are numerous potential distributions which could describe a set of data and the techniques for fitting one of them to the data and evaluating the goodness of fit are likewise numerous. To be honest, it is all hopelessly over my head.

Question(s)

Given that the probability of me attaining a level of understanding sufficient to actually model and evaluate the fit of a distribution to my data lingering around $0$,

1. how important is it to verify that a given distribution, such as the normal distribution, actually models my data?
2. If it is very important and I have no idea how to do it, would it be better to just stick with non-parametric tests which seem to make no assumptions about the shape of the data?

Supplemental

Note that I do understand the importance and usefulness of distributions, I just don't understand how to ensure that a given distribution really represents my data, so I would be very interesting in defaulting to non-parametric tests as an alternative to avoid invalid conclusions due to inappropriately assuming a distribution.

Background

As I understand it (as a novice), parametric tests don't use your actual data as input but rather parameters extracted from your data which describe a distribution. The tests use the distributions which are modelled on your data.

Many researchers who have a limited understanding of the technical details of statistics learn about the normal distribution and that's about it. From what I have read, there are numerous potential distributions which could describe a set of data and the techniques for fitting one of them to the data and evaluating the goodness of fit are likewise numerous. To be honest, it is all hopelessly over my head.

Question(s)

Given that the probability of me attaining a level of understanding sufficient to actually model and evaluate the fit of a distribution to my data lingering around $0$,

1. how important is it to verify that a given distribution, such as the normal distribution, actually models my data?
2. If it is very important and I have no idea how to do it, would it be better to just stick with non-parametric tests which seem to make no assumptions about the shape of the data?

Supplemental

Note that I do understand the importance and usefulness of distributions, I just don't understand how to ensure that a given distribution really represents my data, so I would be very interesting in defaulting to non-parametric tests as an alternative to avoid invalid conclusions due to inappropriately assuming a distribution.

Also, the fields I'm alluding to typically have $n < 30$ or so.

Background

As I understand it (as a novice), parametric tests don't use your actual data as input but rather parameters extracted from your data which describe a distribution. The tests use the distributions which are modelled on your data.

Many researchers who have a limited understanding of the technical details of statistics learn about the normal distribution and that's about it. From what I have read, there are numerous potential distributions which could describe a set of data and the techniques for fitting one of them to the data and evaluating the goodness of fit are likewise numerous. To be honest, it is all hopelessly over my head.

Question(s)

Given that the probability of me attaining a level of understanding sufficient to actually model and evaluate the fit of a distribution to my data lingering around $0$,

1. how important is it to verify that a given distribution, such as the normal distribution, actually models my data?
2. If it is very important and I have no idea how to do it, would it be better to just stick with non-parametric tests which seem to make no assumptions about the shape of the data?

Background

As I understand it (as a novice), parametric tests don't use your actual data as input but rather parameters extracted from your data which describe a distribution. The tests use the distributions which are modelled on your data.

Many researchers who have a limited understanding of the technical details of statistics learn about the normal distribution and that's about it. From what I have read, there are numerous potential distributions which could describe a set of data and the techniques for fitting one of them to the data and evaluating the goodness of fit are likewise numerous. To be honest, it is all hopelessly over my head.

Question(s)

Given that the probability of me attaining a level of understanding sufficient to actually model and evaluate the fit of a distribution to my data lingering around $0$,

1. how important is it to verify that a given distribution, such as the normal distribution, actually models my data?
2. If it is very important and I have no idea how to do it, would it be better to just stick with non-parametric tests which seem to make no assumptions about the shape of the data?

Supplemental

Note that I do understand the importance and usefulness of distributions, I just don't understand how to ensure that a given distribution really represents my data, so I would be very interesting in defaulting to non-parametric tests as an alternative to avoid invalid conclusions due to inappropriately assuming a distribution.