Statistics is a field of science that is involved in the study of data. From the definition perspective, it seems to be a simple and easy course. However, you need to delve deep into its wide range of concepts and identify the statistical methods available. Below are some of the important topics in statistics.
In statistics, the arithmetic mean is the value which is almost equidistant from all other values in a data set. That is, when all values are considered with each other, the mean is the value that represents the minimum possible distance among all of them. Thus, it is the measure of the central tendency, and also the proper average when doing statistical analyses at each interval or ratio levels.
It is the most popular measure of central tendency. It is highly sensitive to extreme values when the data set is skewed to the point of results becoming ridiculous. A good measure of dispersion around the mean is the standard deviation whereas the excellent graphic for the mean is the histogram.
This is a useful graph for representing the median, inter-quartile range and extreme cases. The chart does not necessarily represent the distribution of ranked data. It only represents the above statistics within the distance of scores of the variable of reference. The box plot illuminates the ordinal statistics on the scale on which the variable is expressed.
Due to this, the box plot can be used to identify extreme cases and outliers, assess the skewness of the distribution and so forth. It also informs about the distribution of scores on the natural scale of the variable.
This is a set of technologies where formulas and procedures used for describing mathematical properties in a database. A data set is always specific to a particular group. Descriptive statistics are also bound to such a particular group.
What this entails is they can always say something about such groups, independently of whether the data is usable or not. That is, the statistics always describe the properties of the particular group under research as a sample. They are often used for assessing the quality of the data in the data set and thus, for ascertaining if it is appropriate to continue further into using inferential statistics.
Gigerenzer's modification of Fischer's procedure does not lead to a proper test of significance. It is an attempt to highlight the limitations of the statistical analysis of significance. It needs to be understood as a reaction towards the spread of pseudoscientific null hypothesis, significance testing procedure, and against the popularity of significance testing over other processes which may be more relevant in fields of psychology. This particular topic contains three methods.
These are procedures that are used to assess the probability of a hypothesis against one more alternative hypothesis. Although the test itself can only determine the likelihood of data, Neyman and Pearson assured all theories to be based on, thus deduced from real populations.
Therefore, data can help you make a decision, acting as if they proved or disapproved the hypothesis. Unfortunately, Neyman and Pearson called their initial hypothesis as the null hypothesis against which one or more alternative hypothesis would be tested.
Bayes' theorem is a procedure that tests the hypothesis. It requires prior knowledge of the probability of events and if appropriate, of the instruments with which those events are observed. Null hypothesis significance testing is a pseudoscientific approach to hypothesis testing based on the mix up of the above three procedures.
In statistics, the median is the value that marks the middle of a ranked distribution of scores. It also divides such distribution exactly in its half, so that 50% of the distribution is below the media and the remaining 50% sticks above the medium. Therefore, the median is a measure of central tendency, and it's also the proper average when doing statistical and uses at ordinal levels.
This is the most frequent value in a distribution of scores. The mode can be considered as a frequency statistic which identifies the most typical value in distribution and as a suitable average when doing statistical analysis at nominal levels. It's also a descriptive statistic that describes the most common value(s) in a data set. Not only is it a suitable measure of dispersion for the mode in modal dispersion, but it also offers an excellent graphic for pictographs.
Ultimately as you can see, the subject requires a lot of effort and commitment from the student to get the info in one's fingertips. Wondering where you can get statistics math answers, Feel free to contact us today for prompt help.