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1. What are descriptive and inferential statistics?

   • The analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.

2. What are their differences?

   • Descriptive statistics are applied to populations, and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population (i.e., everybody you are interested in).
   • Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn. It is, therefore, important that the sample accurately represents the population

3. There are two general types of statistic that are used to describe data:

   • Measures of central tendency
     • These are ways of describing the central position of a frequency distribution for a group of data.
   • Measures of spread
     • These are ways of summarizing a group of data by describing how spread out the scores are.

4. Descriptive statistics rely solely on this set of data, whilst inferential statistics also rely on this data in order to make generalisations about a larger population.

5. Descriptive statistics are limited in so much that they only allow you to make summations about the people or objects that you have actually measured. You cannot use the data you have collected to generalize to other people or objects

6. There are two main limitations to the use of inferential statistics.

   • The first, is that you are providing data about a population that you have not fully measured, and therefore, cannot ever be completely sure that the values/statistics you calculate are correct.
   • The second limitation is connected with the first limitation, inferential tests require the user to make educated guesses to run the inferential tests. Again, there will be some uncertainty in this process, which will have repercussions on the certainty of the results of some inferential statistics.

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