This document discusses key considerations for data analysis, including the type of data (nominal, ordinal, interval, ratio scales), objectives, and hypotheses. It covers descriptive statistics like measures of central tendency and variability. Inferential statistics are separated into parametric and non-parametric methods. Parametric tests like the t-test are used for interval/ratio data with normal distributions and large sample sizes, while non-parametric tests like the chi-square test allow for nominal/ordinal data. Specific tests discussed include chi-square for independence and goodness of fit, t-tests, z-tests, F-tests, and ANOVA for comparing groups. Steps for testing hypotheses and levels of statistical significance are also outlined.

1. DATA ANALYSIS

2. DATA ANALYSIS
 Why ?
 How ?
 Some important considerations before analysis
a) Type of data
b) Objectives
c) Hypotheses
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3. CONSIDERATION 1
TYPES OF DATA (SCALES)
 Nominal
 Ordinal
 Interval
 Ratio
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4. CONSIDERATION 2
OBJECTIVES
 What are we trying to find out ?
 In order to achieve this, what kind of information is
required ?
 Which tools are giving this information ?
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5. CONSIDERATION 3
HYPOTHESES
 What is the type of hypotheses framed ?
 What is the level of significance set ?
 What type is the available data ?
 Which technique will meet the research needs
keeping the type of data in mind ?
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6. DESCRIPTIVE ANALYSIS
 Measures of Central Tendency ---
Mean, Median, Mode
 Measures of Variability/Dispersion ---
Range, Average deviation, Quartile deviation,
Standard deviation
 Measures of Correlation ---
 Normal distribution ---
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7. INFERENTIAL STATISTICS
Types of Inferential Statistics
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PARAMETRIC
NON
PARAMETRIC

8. PARAMETRIC & NON PARAMETRIC
STATISTICS
Conditions Parametric Non Parametric
Type of Data Interval or Ratio Nominal or Ordinal
Variance in
all groups
Equal
Need not be Equal
Distribution
of Trait
Normal May not be Normal
Sampling Probability Non Probability
Sample Size Large Small
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9. TESTING OF HYPOTHESES
 If the calculated value of a given statistic is lesser
than the Table value of that statistic, then the null
hypothesis is RETAINED.
 If the calculated value of a given statistic is greater
than the Table value of that statistic, then the null
hypothesis is REJECTED.
 Calculated value > Table value (REJECT)
 Calculated value < or = Table value (RETAIN)
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10. TAILS OF A TEST
 For Null hypotheses two tailed test is applied.
 For Non--directional hypotheses two tailed test
is applied.
 For Directional hypotheses one tailed test is
applied.
Can you tell WHY ?????
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11. STATISTICAL SIGNIFICANCE
What is the significance of SIGNIFICANCE?
Levels of Significance---
1) 0.05 & 2) 0.01
Levels of Confidence---
1) 95 % & 2) 99 %
When to use the terms --- Significance & Confidence ?
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12. STEPS OF TESTING HYPOTHESES
 Frame the hypothesis
 Choose the appropriate statistical test
 Decide the level of significance
 Calculate the value of the statistic
 Refer to the appropriate Table & get the critical
value
 Compare both the values & decide about the
significance of your results
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13. SOME MAJOR STATISTICAL TESTS
 Chi square test
 t test
 Z test
 F test
 ANOVA
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14. CHI SQUARE TEST
Non-parametric test
Used when the data is in the nominal scale or
grouped in the nominal categories.
Goodness of Fit
Do the people differ significantly in their opinion?
Is the opinion equally divided?
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YES NO
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15. CHI SQUARE TEST
Gender
Favourable
Opinion
Unfavourable
Opinion
Men 53 37
Women 64 46
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Test of Independence
Do men & women differ significantly in their
opinions? Are Gender and Opinion independent of
each other?

16. Z TEST & T - TEST
Parametric tests are used when you want to
compare sample statistics of two groups.
Comparison of means, s.d., percentages of TWO
groups.
Z test is used when the sample size is large (> 30 )
&
t test is used when the sample size is small (30 or
< 30)
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17. F TEST
 Parametric test
 When more than two groups are to be compared
F Test is applied
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18. ANOVA
 Parametric test
 Analyses the differences between group means
and their "variation" among and between groups
 ONE independent variable ONE WAY ANOVA
 TWO independent variables TWO WAY ANOVA
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19. THANK YOU
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