Honors Biology Mr. Luis A. Velázquez
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Honors Biology Mr. Luis A. Velázquez
Qualitative vs. Quantitative Data
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Qualitative vs. Quantitative Data
• Quantitative data is information about quantities; that is, information that can be measured and written down with numbers.
• Qualitative data is information about qualities; information that can't actually be measured.
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• The age of your car.
• The number of hairs on your knuckle.
• The softness of a cat.
• The color of the sky.
• The number of pennies in your pocket.
Qualitative vs. Quantitative Data(Quantitative.)
(Quantitative.)
(Qualitative.)
(Qualitative.)
(Quantitative.)
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Categorical Data
• This is data that can be organized into mutually exclusive categories.
• If we look at a bunch of bananas and they're all either green, brown, yellow or blue, then we could use the categories "green," "brown," "yellow" and "blue" to record our data.
• Categorical data is usually qualitative. However, quantitative data can also be put into categories.
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Raw Data• Unanalyzed data; data not
yet subjected to analysis.
• Raw data is never is use in a graph.
• Also known as primary data.
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Raw Data• according to statistics, the details given by
investigator or collected from sources are known as raw data.
• In other words its the first hand information undergone no mathematical or statistical treatment also called as raw data.
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Results vs. Conclusion• Conclusion and Results are two terms used in thesis
writing and surveys or experiments respectively. • Conclusion forms the end part of a thesis or a
dissertation. • Results form the end part of a survey or a chemical
experiment. This is one of the main differences between conclusion and results.
•
Read more: http://www.differencebetween.com/difference-between-conclusion-and-vs-results/#ixzz2fMSoh3ER
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• Conclusion aims at the briefing of the research findings of the researcher. It should be short and concise.
• It should contain concise and short paragraphs.
• A conclusion should not contain long paragraphs.
• Results can be statistical in composition and sometimes descriptive too. If they are descriptive in nature then they can contain long paragraphs.
Read more: http://www.differencebetween.com/difference-between-conclusion-and-vs-results/#ixzz2fMTS2L5q
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Null Hypothesis• The simplistic definition of the null is as the opposite of
the alternative hypothesis.• The null hypothesis (H0) is a hypothesis which the
researcher tries to disprove, reject or nullify.• The 'null' often refers to the common view of something.• The alternative hypothesis is what the researcher really
thinks is the cause of a phenomenon.
Read more: http://explorable.com/null-hypothesis
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• An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.
• Despite this, many researchers neglect the null hypothesis when testing hypotheses, which is poor practice and can have adverse effects.
Read more: http://explorable.com/null-hypothesis
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A researcher may postulate a hypothesis:• H1: Tomato plants exhibit a higher rate of growth when planted
in compost rather than in soil.
And a null hypothesis:• H0: Tomato plants do not exhibit a higher rate of growth when
planted in compost rather than soil.