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Counts Menu

The Counts menu selection has four tests that can be performed for simple frequency data. The chi-square test is used to analyze a contingency table consisting of rows and columns to determine if the observed cell frequencies differ significantly from the expected frequencies. Fisher's exact test is similar to the chi-square test except it is used only for tables with exactly two rows and two columns. The binomial test is used to calculate the probability of two mutually exclusive outcomes. The poisson distribution events test is used to describe the number of events that will occur in a specific period of time.

The Counts menu has four selections:

Chi-square test

The chi-square is one of the most popular statistics because it is easy to calculate and interpret. There are two kinds of chi-square tests. The first is called a one-way analysis, and the second is called a two-way analysis. The purpose of both is to determine whether the observed frequencies (counts) markedly differ from the frequencies that we would expect by chance.

The observed cell frequencies are organized in rows and columns like a spreadsheet. This table of observed cell frequencies is called a contingency table, and the chi-square test if part of a contingency table analysis.

The chi-square statistic is the sum of the contributions from each of the individual cells. Every cell in a table contributes something to the overall chi-square statistic. If a given cell differs markedly from the expected frequency, then the contribution of that cell to the overall chi-square is large. If a cell is close to the expected frequency for that cell, then the contribution of that cell to the overall chi-square is low. A large chi-square statistic indicates that somewhere in the table, the observed frequencies differ markedly from the expected frequencies. It does not tell which cell (or cells) are causing the high chi-square...only that they are there. When a chi-square is high, you must visually examine the table to determine which cell(s) are responsible.

When there are exactly two rows and two columns, the chi-square statistic becomes inaccurate, and Yate's correction for continuity is usually applied. Statistics Calculator will automatically use Yate's correction for two-by-two tables when the expected frequency of any cell is less than 5 or the total N is less than 50.

If there is only one column or one row (a one-way chi-square test), the degrees of freedom is the number of cells minus one. For a two way chi-square, the degrees of freedom is the number or rows minus one times the number of columns minus one.

Using the chi-square statistic and its associated degrees of freedom, the software reports the probability that the differences between the observed and expected frequencies occurred by chance. Generally, a probability of .05 or less is considered to be a significant difference.

A standard spreadsheet interface is used to enter the counts for each cell. After you've finished entering the data, the program will print the chi-square, degrees of freedom and probability of chance.

Use caution when interpreting the chi-square statistic if any of the expected cell frequencies are less than five. Also, use caution when the total for all cells is less than 50.

Example

A drug manufacturing company conducted a survey of customers. The research question is: Is there a significant relationship between packaging preference (size of the bottle purchased) and economic status? There were four packaging sizes: small, medium, large, and jumbo. Economic status was: lower, middle, and upper. The following data was collected.

  Lower Middle Upper
Small 24 22 18
Medium 23 28 19
Large 18 27 29
Jumbo 16 21 33

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Chi-square statistic = 9.743
Degrees of freedom = 6
Probability of chance = .1359

Fisher's exact test

The chi-square statistic becomes inaccurate when used to analyze contingency tables that contain exactly two rows and two columns, and that contain less than 50 cases. Fisher's exact probability is not plagued by inaccuracies due to small N's. Therefore, it should be used for two-by-two contingency tables that contain fewer than 50 cases.

Example

Here are the results of a public opinion poll broken down by gender. What is the exact probability that the difference between the observed and expected frequencies occurred by chance?

  Male Female
Favor 10 14
Opposed 15 9

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Fisher's exact probability = .0828

Binomial test

The binomial distribution is used for calculating the probability of dichotomous outcomes in which the two choices are mutually exclusive. The program requires that you enter the number of trials, probability of the desired outcome on each trial, and the number of times the desired outcome was observed.

Example

If we were to flip a coin one hundred times, and it came up heads seventy times, what is the probability of this happening?

Number of trials: 100
Probability of success on each trial (0-1): .5
Number of successes: 70

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Probability of 70 or more successes < .0001

Poisson distribution events test

The poisson distribution, like the binomial distribution, is used to determine the probability of an observed frequency. It is used to describe the number of events that will occur in a specific period of time or in a specific area or volume. You need to enter the observed and expected frequencies.

Example

Previous research on a particular assembly line has shown that they have an average daily defect rate of 39 products. Thus, the expected number of defective products expected on any day is 39. The day after implementing a new quality control program, they found only 25 defects. What is the probability of seeing 25 or fewer defects on any day?

Observed frequency: 25
Expected frequency: 39

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Probability of 25 or fewer events = .0226

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