I would like to expand the problem mentioned by Henry. Let's just point out a few problems that may arise:
- People tend to use the supermarket that is close to their homes. As you know, there are different types of people in different areas - and I would expect that the goods bought highly depend on personal education and financial background.
- There are supermarkets with higher prices - it is likely that those supermarkets are visited by different people than the discount supermarkets.
- Mothers with children probably buy at different times than people that do work full-time. But do they buy the same?
In statistical terminology, you will have to take care of the sampling method you use. With every random sample you can make a guess at the distribution it was taken from. However, you have to take care that you take the sample from the correct distribution and not a special subset.
Firstly, on your wording: The population we are talking about is the set of all items that can be bought, not the people living in austria. The population always denotes the possible outcomes of one random sample - and you are observing items bought.
It is hard to tell if you will get enough samples - this will depend on the number of customers you will be able to observe, as well as it will depend on the amount of different goods you record.
Let's have a look at two (very constructed) examples. Say, you record 1000 people each buying exactly one item. In extreme cases, the following might happen:
- All people buy the same product, let's say it is milk. In this case, your sample size should certainly be big enough to conclude that milk is one of the top sold products.
- Everybody buys something different. Then, with this sample size, it will be impossible to determine the most sold product.
This shows that the sample size you have to take on a huge amount depends on the variance you encounter in your data.
Furthermore, the sample sizes depend on the statistical method you will use. Usually, the more you can assume on your distribution, the stronger the method you can use. For example, if you can assume a normal distribution, you may use parametric tests that usually do not need a lot of samples. This is not surprising, as you put a lot of information (normality) as a guess in your data, which leaves only a little bit of information to be determined by the data. However, if you have no information on the distribution, the test will have to guess everything. This naturally means more information will be needed beforehand.
That is why often small sample sizes are taken as a pre-study. Afterwards, you will have a feeling on the variance and will be able to determine the statistical methods that will be used as well as their requirements in terms of sample sizes.
Finally, you should define the groups you are looking for. Is the manufacturer of something important to you? Will you just group 'Cheese', or will there be different groups of cheese?
How would I do it? This really depends on my intention. Do I have a budget? Do I have multiple people taking samples? Maybe there are supermarkets that offer me their product statistics. Maybe it would be an idea to interview the people you recorded to identify differences in personal background. Then you could check whether this differences influence the output. Furthermore, it might be worth doing a small study first to identify further problems that may arise with sampling and data recording.
As you are looking at Austria, I assume you speak German. Which means I can point you to a book that I do not yet have read in total but that might bring up a lot of questions relevant to your problem. It is called "Stichproben" by Kauermann and Küchenhoff. Sorry for all the english readers around here, I do not know an english book about that topic...