Transcript Quantitative vs Qualitative Data

Qualitative vs. Quantitative Data
Honors Biology
Mr. Luis A. Velázquez
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.
Qualitative vs. Quantitative Data
• The age of your car.
(Quantitative.)
• The number of hairs on your knuckle.
• The softness of a cat.
• The color of the sky.
(Quantitative.)
(Qualitative.)
(Qualitative.)
• The number of pennies in your pocket.
(Quantitative.)
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.
Raw Data
• Unanalyzed data; data not
yet subjected to analysis.
• Raw data is never is use in
a graph.
• Also known as primary
data.
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.
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/differencebetween-conclusion-and-vs-results/#ixzz2fMSoh3ER
• 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/differencebetween-conclusion-and-vs-results/#ixzz2fMTS2L5q
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
• 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
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.