If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size.

The frequency table records the number of observations falling in each interval. Frequency tables are useful for analyzing categorical data and for screening data for data entry errors. Note that we will refer to two types of categorical variables: Categorical and Grouping or Break.

The normal distribution, also known as a Gaussian distribution or “bell curve” is the most common frequency distribution. This distribution is symmetrical, with most values falling towards the centre and long tails to the left and right. It is a continuous distribution, with no gaps between values.

The Shapiro-Wilk tests if a random sample came from a normal distribution. The null hypothesis of the test is the data is normally distributed. If the p value returned is less than . 05 , then the null hypothesis is rejected and there is evidence that the data is not from a normally distributed population.

The data values. What can be lost by presenting data in a frequency distribution? The number of data values in each data group.

Frequency distributions can show either the actual number of observations falling in each range or the percentage of observations. In the latter instance, the distribution is called a relative frequency distribution. Frequency distribution tables can be used for both categorical and numeric variables.

How you do this:

1. Count the total number of items. In this chart the total is 40.
2. Divide the count (the frequency) by the total number. For example, 1/40 = . 025 or 3/40 = . 075.

An easy way to define the difference between frequency and relative frequency is that frequency relies on the actual values of each class in a statistical data set while relative frequency compares these individual values to the overall totals of all classes concerned in a data set.

: the ratio of the frequency of a particular event in a statistical experiment to the total frequency.

Relative frequency is used when probability is being estimated using the outcomes of an experiment or trial, when theoretical probability cannot be used. For example, when using a biased dice, the probability of getting each number is no longer .

8 Real Life Examples Of Probability

• Weather Forecasting. Before planning for an outing or a picnic, we always check the weather forecast.
• Batting Average in Cricket.
• Politics.
• Flipping a coin or Dice.
• Insurance.
• Are we likely to die in an accident?
• Lottery Tickets.
• Playing Cards.

To find the relative frequencies, divide each frequency by the total number of students in the sample–in this case, 20. Relative frequencies can be written as fractions, percents, or decimals. Relative frequency = frequency of the classtotal.

Subjective probability is where you use your opinion to find probabilities. For example: You think you have an 80% chance of your best friend calling today, because her car broke down yesterday and she’ll probably need a ride.

There are three major types of probabilities:

• Theoretical Probability.
• Experimental Probability.
• Axiomatic Probability.

Objective probability is the probability an event will occur based on an analysis in which each measure is based on a recorded observation or a long history of collected data. In contrast, subjective probability allows the observer to gain insight by referencing things they’ve learned and their own experience.

The complement of an event is the subset of outcomes in the sample space that are not in the event. A complement is itself an event. An event and its complement are mutually exclusive and exhaustive. This means that in any given experiment, either the event or its complement will happen, but not both.