Simple understanding of biostatistics

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Biostatistics

Dr. Hamdi Abdulwahab AlhakimiAlbaha University

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By the end of these session, attendants should:• Understand the basic concepts of biostatistics.• Differentiate between descriptive and

inferential biostatistics. • Clearly identify types of data.

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List of contents• Statistics: definitions and uses.• Data: types and distributions.• Descriptive statistics: calculations, tabulations &

graphs.• Normal distribution: characteristics & uses.• Inferential statistic: hypothesis testing & estimation.• How to choose appropriate statistical procedures?• Interpretation of statistics used in scientific papers.

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Statistics

• The doing of statistics is a way of thinking about numbers (collection, analysis, and presentation), with intention to relate their meaning to the objectives for which they are collected.

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• Formulas are only a part of that thinking, simply tools of the trade; they are needed but not as the only things one needs to know.

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Biostatistics

Basically, a successful research should consist of:

(1) Good research question.(2) Investigation (Calibration & Sampling).(3) Presentation of results (descriptive & inferential

statistics).

(4)Conclusions (inferential statistics).

the last three elements; together they form a field called biostatistics.

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DATARESULTS

+ conclusionsBiostatistics

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Biostatistics

Descriptive Statistics

Inferential statistics

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Biostatistics


graphstabulationscalculations

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graphstabulationscalculations

- Proportions, rates & ratios.

- Measures of central tendency (Mean, Mode & Median).

- Measures of dispersion (S.d,

range).-correlation coefficient

- Regression coefficient.

- Quantiles.

- Frequency distribution

tables.

- Cross tabs.

- stem & leaf diagram.

- Bar graphs.

-Pie chart.

- Histogram.

- Scatter plot.

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Biostatistics


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Hypothesis Testing

Estimation

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Hypothesis TestingEstimation

• Standard Error

Confidence Interval

• Significance Tests

P value

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Important Note:

“A poor or invalid statistical analysis can be repeated using correct methods but no amount of data manipulation can compensate for invalid data.”

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• Data are set of observations, measurements or counts …..ect which have not meaning alone.

• Data >>>>> Information >>>>> Intelligence

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DATA

quantitativeContinuous(measurements)

Discrete(counts)

Dichotomous

Polychotomous

Types of Variables

• (Quantitative) Numerical variables:– Always numbers– Examples: age in years, weight, blood pressure readings,

temperature, concentrations of pollutants and, counts of cases per week other measurements

• (qualitative) Categorical variables:– Information that can be found into categories– Types of categorical variables – ordinal, nominal and

dichotomous (binary)

Categorical Variables:Nominal Variables

• Nominal variable – a categorical variable without an intrinsic order

• Examples of nominal variables:– Residence (Northeast, South, Midwest, etc.)– Sex (male, female)– Nationality (American, Mexican, French)– Race/ethnicity (African American, Hispanic, White, Asian

American)– Favorite pet (dog, cat, fish, snake)

Categorical Variables:Dichotomous Variables

• Dichotomous (or binary) variables – a categorical variable with only 2 levels of categories– Often represents the answer to a yes or no question

• For example:– “Did you attend the church on May 24?”– “Did you eat potato salad ?”– Anything with only 2 categories

Categorical Variables:Ordinal Variables

• Ordinal variable—a categorical variable with some intrinsic order

• Examples of ordinal variables:– Education (no high school degree, HS degree, some

college, college degree)– Agreement (strongly disagree, disagree, neutral, agree,

strongly agree)– Rating (excellent, good, fair, poor)– Frequency (always, often, sometimes, never)

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Question:

• If we conducted a research and collected a tremendous amount of data how could we deal with these data, in order to present results and draw conclusions???

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Answer

• You can use descriptive statistics to present the findings of study according to the type of data.

• You can use inferential statistics to draw conclusions.

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Use of descriptive Statistics in

qualitative data

graphstabulations

calculations

- Proportions, rates & ratios.

- Frequency distribution

tables.

- Cross tabs.

- Bar graphs.

-Pie chart.

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Descriptive statistics in qualitative data

• Proportions: percentages, specificity, sensitivity …ect.• Rates: prevalence, incidence, mortality, morbidity ,

fatality rates, change rates …ect.• Ratios: - odds ratio. - relative risk. - S.M.R

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Use of descriptive Statistics in quantitative

graphscalculations

- Measures of central tendency (Mean, Mode & Median).

- Measures of dispersion (S.d,

range).-correlation coefficient

- Regression coefficient.

- Quintiles.

- Histogram.

- Scatter plot.

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Description of Quantitative Data

- If normally distributed Mean & S.d.

- If not normally distributed Median&range.

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Story of standard deviation

• Average deviation = SUM(X.-Mean)/n

• Variance = SUM(X.-Mean)2/n-1

• Standard Deviation = square root of variance.

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Distribution of Data

“The way in which the observations distribute themselves over the range of possible values.”

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• Fortunately, a small number of Distributions tend to occur frequently;

- Binomial Distribution. - Poisson Distribution. - Student t-distribution. - Normal Distribution. - Skewed Distributions. - Log-distribution. - Bimodal Distribution.

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DATA

Quantitative

Continuous

- Normal Distribution

- Non-normal Distributions

Discrete

Poisson Distribution

Qualitative

Dichotomous

Binomial Distribution

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WHY NORMAL DISTRIBUTION?

Because:- For a large sample the binomial distribution

approximates to a normal distribution.

-The Poisson distribution approximates to a normal distribution with mean=variance.

- Non-normal distributions (skewed & log) could be transformed into normal distribution.

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Normalizing Transformation

• Normalizing transformations are a very powerful weapon in the statistical armory.

- positively skewed distribution by taking square root of each observation.

- Log distribution by taking the natural logarithm of each observation.

- Negatively skewed by reciprocation.

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Properties of Normal Distribution:

- Bell-shaped symmetrical around the mean. - Totally described by its mean & standard deviation. - Mean=Mode=Median. - 68.2% of observations lie within 1 standard deviation. - 95% of observations lie within 1.96 (or tow) standard

deviations. - 99.9% of observations lie within 3 standard

deviations.

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Question

• You conducted a research about the association between smoking & blood pressure. How do you know that raw data you collect about blood pressure are normally distributed???

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Answer

• Doing a histogram for data about blood pressure by using SPSS.

• Using statistical procedures like: “Siminirov kilomigrov”

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Inferential Statistics

Questions:

• Is the descriptive statistics enough?

• What is the additional benefit of inferential statistics over the descriptive statistics??

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Example:

• If the results if your research were: “the relative risk of smoker to be hypertensive

is 3 times greater than non-smoker”

Is that enough to say the smoking will increase the risk of hypertension?

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Answer:

• Inferential statistics enable us to do generalization of our descriptive statistics.

• Inferential statistics make us able to say that our results are not due to chance.

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Hypothesis TestingEstimation

• Standard Error

Confidence Interval

• Significance Tests

P value

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Hypothesis Testing(Basic Concepts)

• Statistics are findings calculated from a sample.

• Parameters are their population-based counterparts; these are unknown values.

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• If a sample is representative of a population, a statistics will actually give similar but not the same parameters of population.

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• Representative sample means it: - reflects the actual variation present in

population by random sampling.

- is big enough by calculation of sample size.

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• Hypotheses: we have two statements about the population;

• Null hypothesis -- hypothesis of no difference or no effect.

• Alternative hypothesis -- Research hypothesis; hypothesis of a difference or effect.

• Note: we use parameters of population here.

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“The P-value is the probability of the observed data or more extreme outcome would have occurred by chance alone if the null hypothesis is true.”

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• Level of significance is a cut-off point to which a P-value is judged; usually 0.05 or 0.01.

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Steps of Hypothesis Testing

1- Statement of null & alternative hypotheses.2- Determination of the level of significance.3- Choosing of appropriate significance test and

calculation of “test statistic.”4- Calculation of “P-value.”5- Making decision on accepting or rejecting null

hypothesis.

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Decision rule

• P-value < significance level: - rejecting null hypothesis. - results are significant. - results does not occur by chance.

• P-value > or = significance level: - not rejecting null hypothesis. - results are not significant. - results occur by chance.

Simple understanding of biostatistics

Health & Medicine

Transcript of Simple understanding of biostatistics