Numerous graphs and tables contain false information, mislabeled or misleading information, or they just do not have the essential information needed by the reader to important decisions on what is presented.

Pie charts are good for showing how the categorical data is broken down, but they can be tricky. Here's how to evaluate the quality of a pie chart:

✓ Check that the percentages add up to 100%, or close to it (possible round-off error should be small).

✓ Change segment labeled "Other" which is larger than the rest of the cut. This means that the pie is too vague.

✓ Watch out for deformations with three-dimensional pie chart form, where the slice closest to you seem larger than it really is because of the angle at which it is presented.

✓ Search for reported a total number of individuals who were part of the pie chart, so you can determine "how much" is the cake, so to speak. If the sample is too small, the results are not going to be reliable.

A bar chart breaks categorical data by number or percentage of each group. When dealing with a bar graph:

✓ Consider the units are represented by the height of the bar and what the results mean in terms of units. For example, the total number of crimes verses crimes (total number of crimes per capita).

✓ Check the suitability of the level, or the amount of space between the unit used to express the number in each group of bar graph. Small scale (for example, going through 1-500 10s) the difference appears greater; large scale (ranging from 1-500 100s) make them look smaller.

A time chart that shows how changing certain measurable amount over time, for example, stock quotes. Here are some things to watch in the chart of time:

✓ Note the vertical scale (volume) and the horizontal axis (timeline) axis; These results can be seen more or less dramatically by simply changing the scale.

✓ Keep in mind the units can be played by the card and make sure they are fair to compare over time; for example, dollars adjusted for inflation?

✓ Beware of people trying to explain why a trend has emerged ring without additional statistics to back themselves up.

A time chart shows generally what happens. Why it happens is another story.

✓ Watch out for situations where the time axis is not marked equidistant jumps from one another. This often occurs when the data are missing. For example, the time axis is equal spacing between the 1971, 1972, 1975, 1976, 1978, when it actually empty space of time when no data is available should be displayed.

Histograms graph numeric data in a bar-chart type graph (see Chapter 3). Items to watch about histograms:

✓ Note the scale for the vertical (frequency / relative frequency axis), especially for results exagger-ated or down playable on the use of inappropriate levels.

✓ Check the units on the vertical axis, if they are reporting frequency or relative frequency analysis of information.

✓ Look scale for groups of numerical variables in the horizontal axis. If the group is based in small intervals (eg, 0-2, 2-4, etc.), the data may have too big of a volatility. If the group is based on large intervals (0-100, 100-200, etc.), the data can provide a smoother look than realistic.

Bias in the statistics are the result of a systematic error that either overestimated or underestimated the actual value. Here are some of the most common causes of chaotic data:

✓ Measuring instruments which are systematically off such a scale that always adds 5 pounds to your weight.

✓ The participants affected by the data collection process. Thus, the research will ask: "Did you ever disagree with the government," the proportion of people overestimate dissatisfied with the government?.

✓ A sample of individuals who are not the p-scheme represents interest. For example, evaluation study habits by creating only people visiting the campus library bias.

✓ Researchers at goal. Researchers have gained interest in the results of their study, and rightly so, but sometimes the interest rate effect on these results. For example, I know who got what treatment to an experiment caused bias - ing the double-blind study is the goal.

**pie charts**Pie charts are good for showing how the categorical data is broken down, but they can be tricky. Here's how to evaluate the quality of a pie chart:

✓ Check that the percentages add up to 100%, or close to it (possible round-off error should be small).

✓ Change segment labeled "Other" which is larger than the rest of the cut. This means that the pie is too vague.

✓ Watch out for deformations with three-dimensional pie chart form, where the slice closest to you seem larger than it really is because of the angle at which it is presented.

✓ Search for reported a total number of individuals who were part of the pie chart, so you can determine "how much" is the cake, so to speak. If the sample is too small, the results are not going to be reliable.

**Bar graph**A bar chart breaks categorical data by number or percentage of each group. When dealing with a bar graph:

✓ Consider the units are represented by the height of the bar and what the results mean in terms of units. For example, the total number of crimes verses crimes (total number of crimes per capita).

✓ Check the suitability of the level, or the amount of space between the unit used to express the number in each group of bar graph. Small scale (for example, going through 1-500 10s) the difference appears greater; large scale (ranging from 1-500 100s) make them look smaller.

**Time charts**A time chart that shows how changing certain measurable amount over time, for example, stock quotes. Here are some things to watch in the chart of time:

✓ Note the vertical scale (volume) and the horizontal axis (timeline) axis; These results can be seen more or less dramatically by simply changing the scale.

✓ Keep in mind the units can be played by the card and make sure they are fair to compare over time; for example, dollars adjusted for inflation?

✓ Beware of people trying to explain why a trend has emerged ring without additional statistics to back themselves up.

A time chart shows generally what happens. Why it happens is another story.

✓ Watch out for situations where the time axis is not marked equidistant jumps from one another. This often occurs when the data are missing. For example, the time axis is equal spacing between the 1971, 1972, 1975, 1976, 1978, when it actually empty space of time when no data is available should be displayed.

**Histograms**Histograms graph numeric data in a bar-chart type graph (see Chapter 3). Items to watch about histograms:

✓ Note the scale for the vertical (frequency / relative frequency axis), especially for results exagger-ated or down playable on the use of inappropriate levels.

✓ Check the units on the vertical axis, if they are reporting frequency or relative frequency analysis of information.

✓ Look scale for groups of numerical variables in the horizontal axis. If the group is based in small intervals (eg, 0-2, 2-4, etc.), the data may have too big of a volatility. If the group is based on large intervals (0-100, 100-200, etc.), the data can provide a smoother look than realistic.

**Biased Data**

✓ Measuring instruments which are systematically off such a scale that always adds 5 pounds to your weight.

✓ The participants affected by the data collection process. Thus, the research will ask: "Did you ever disagree with the government," the proportion of people overestimate dissatisfied with the government?.

✓ A sample of individuals who are not the p-scheme represents interest. For example, evaluation study habits by creating only people visiting the campus library bias.

✓ Researchers at goal. Researchers have gained interest in the results of their study, and rightly so, but sometimes the interest rate effect on these results. For example, I know who got what treatment to an experiment caused bias - ing the double-blind study is the goal.

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