eChapters

Section-A : Write True / False

Q.1 A random variable (r.v) is a real number x connected with the outcomes of a random experiment.

Q.2 In tossing of a balanced die at one time, the number coming on the upper most face of the die is a random variable x which takes any one out of 6 values taken as 1, 2, 3, 4, 5, 6.

Q.3 Let s be the sample space associated with a given random experiment. A real valued function defned on s and taking values in R (-∞,∞) is called one dimensional random variable.

Q.4 In general a n dimensional random variable is simply a function whose domain is S and whose range is a collection of n-tuples of real members (vectors in n-space).

Section-A : Write True / False

Q.1 The average value of a random fenamenon is termed as “Mathematical Expectation” or “Expected value” of random fenamenon.

Q.2 The mathematical expression to compute the expected value of a discrete random variable X with its p.m.f. f(x) is def ned as

Section-A : Write True / False

Q.1 The moment generating function (m.g.f) of a random variable X (about origin having the probability function f (x) is defned as

9.1 Section-A : Write True / False

Q.1 The probabilistic laws for dif erent values of a random variable are made on the basis of given conditions. T ese conditions are known as "prior considerations".

Q.2 If the probabilistic laws are made for dif erent values of a random variable on the basis of results obtained from an experiment, then such results are known as "a posterior inferences".9.1.1 Discrete Uniform Distribution

Q.3 A random variable X is said to have a discrete uniform distribution over the range [1, n] if its p.m.f is written as

Section-A : Write True / False

10.1.1 Normal Distribution

Q.1 A r.v. X is said to have a normal distribution with parameters (μ, σ²) if its p.d.f. is given by the probability law

11.1 Section-A : Write True / False

Q.1 Some assumptions or guesses made about the unknown population parameters, is know as "Statistical hypothesis".

Q.2 For the normal distribution N(μ, σ²), the statement (1) μ = 25.0 and (ii) σ² = 37.0, are known as statistical hypotheses.

Q.3 Let p = 0.5 is the probability of getting a head in single throw of a coin. Ten, this statement is not a statistical hypothesis.

Q.4 The statistical hypothesis under consideration (under test), is known as "Null hypothesis".

Q.5 The null hypothesis is denoted by "Ho".

12.1 Section-A : Write True / False

Q.1 Almost all biological data e.g. crop yield, plant height, seed weight, seed size, protein content in pulse, oil content in oil seed etc. are assumed to be normaly distributed.

Q.2 Let X be a normal variable with N(μ, σ²). The random variable Z def ned as Z = (x - μ)/σ, is known as standard normal variable.

Q.3 The standard normal variable Z is denoted as Z ~ N (0, 1).

Q.4 The p.d.f. of random variable Z is given as

13.1 Section-A : Write True / False

Q.1 The square of a standard normal variate is known as Chi-square variate with one d.f.

Q.2 If X ~ N(μ, σ²) and Z = (x - μ)/σ then Z² is known as Chi-square variate with 1.d.f.

Q.3 If Xi ~ N(μi, σi), i = 1,2,...., n are n independent normal variates then

14.1 Section-A : Write True / False

Q.1 If sample size n is small ie n < 30, then the small sample test is applicable.

Q.2 The exact sample test (small sample test) can be applied to large sample test but the converse is not plaussible.

Q.3 If (x²1 , x²2 ) are two independent chi-square variates with (V1, V₂) degree of freedom then F statistic is def ned as

15.1 Section-A : Write True / False

Q.1 In bivariate distribution if the change in one variable afects a change in the other variable, the variables are said to be correlated.

Q.2 If the change in both the variables takes place in the same direction then the variables are said to have a positive or direct correlation.

Q.3 The pair of variables say, Income and expenditure in house hold or the height and weight of a group of children possess the direct correlation between them.

Q.4 If the change in both the variables takes place in oposite directions, then the variables are said to have "Negative or inverse" correlation.

Q.5 The pairs of variables say (i) Price and demand of a commodities or (ii) Volume and pressure of a perfect gas" have a negative correlation.

Q.6 The two variables are said to have a perfect positive correlation if the deviation in one variable is followed by a corresponding and proportionate deviation in the other variable.

Q.7 There are some funny combinations of variables between which the correlation are said to be "spurious or nonsense".

ABSTRACT
Methanogenesis is a ubiquitous process primarily encountered in anaerobic environments. Methanogens are a group of diverse obligate anaerobic bacteria that produce methane from complex organic matter. Despite their differences in morphological characters and habitat, they show coherence in terms of methane-producing metabolic activity. Being distinctive from all other classical prokaryotes, these are grouped under another kingdom of Archea. Other distinctions of the members are based on their unique cell wall structures and membrane components, as well as differences in their highly conserved gene sequences. Methanogens likely owe their cosmopolitan status to their unique mode of energy metabolism of methane generation. They are active in various ecological niches, from rumens of animals to submarine volcanic vents, sewage digestors and landfills. Methanogenic bacteria are abundant in habitats where high affinity electron acceptors are negligible. Methane fermentation involves bacteria that obtain energy by catalysing anaerobically degradable organic matter to CH₄ and CO₂ by consuming H₂, CO₂, and low-carbon organic acids as their substrates. The gaseous products of methanogenesis are greenhouse gases, thus are major contributors to global warming. Methanogens could be used to produce renewable carbon-neutral energy substitute, biogas. Methane cycling archaea play a major role in global methane production as energy for the future.

ABSTRACT
Methanogens are primarily anaerobic Archaebacteria representing the most primitive earthly creatures, alive since about 3.5 billion years. While oxygen inhibit their activity, they adapt well in a variety of ecological niche, viz., the intestinal tracts of insects and animals including ruminants, sewage digesters, groundwater and deep soil/water, etc. They are involved in biomethanation that converts organic polymers, including the complex recalcitrant lignocelluloses, to CH₄ and CO₂. Enhancing the process by optimising the physical, chemical and molecular parameters is an interesting biotechnology. Molecular research is rapidly progressing on the genomics, gene and its regulation, and methanogenesis as it expresses. Identifying novel species expressing methanogenesis and manipulating the genetic make-up including through cloning in recent years will set goals and leads for future investigations. The biomethanation enzyme Methyl-CoM-reductase (Mcr) constitutes about 10% of the methanogen protein. Due to its abundance and significance, elucidating its structure and its synthesis and regulation mechanism has received much attention recently. Mcr-coding genes from methanogens have been cloned and sequenced. An approach at the molecular level to enhance biomethanation seems to lie in metagenomics. The microbiology, biochemistry, the roles of the participating microbes, their molecular biology and applications in methanogenesis are discussed in this chapter.

ABSTRACT
Methanogenesis is an important biogeochemical process takes place in anaerobic ecological sites. It is the last step of terminal electron accepting process of anaerobic respiration. Methanogens synthesise CH₄ using acetate and CO₂ as precursor molecules. Former types of methanogens are referred as acetoclastic methanogens and the later ones as hydrogenotrophic methanogens. After biogenesis of CH₄, it is emitted to atmosphere and causes global warming. In aerobic ecosystem CH₄ is oxidised by methanotrophs to CO₂ a less global warming molecule. However, in anoxic environment CH4 acts as an electron donor and interacts with various electron acceptors. Methane modulates denitrification, Fe reduction, SO₄ reduction and organic matter decomposition. Manuscript elucidates methanogenesis and highlights the pathways of interaction between methanogenesis and cycling of electron acceptors in various anaerobic ecosystems in relation to global C and N balance.

ABSTRACT
Biomethanation comprises of four interdependent stages in series, hydrolysis, acidogenesis, acetogenesis and methanogenesis. Methanogenesis, the final stage of biomethanation, is accompanied with the reduction of substrates that include organic acids such as CH₃COOH, and others such as CO₂ and CH₃OH to CH₄ by methanogenic and methanotrophic archaea, usually under anaerobic conditions. Strictly anaerobic methanogenic archaea are found in the ruminant intestine, sewage digester, groundwater and deep soil/water etc. In methanogenesis, H₂ acts as the electron donor while substrates CO₂, CH₃OH and probably CoM-S-S-CoB (for CH₃COOH) act as terminal electron acceptors and reduce to methane, a process known as anaerobic respiration. This conversion is associated with the electrogenic proton motive force along with sodium motive force to drive the ATP synthesis by A0A1 ATP synthase, known as respiratory phosphorylation. The mechanism of conversion of methane from CO₂ by methanogens with cytochromes is same as those without cytochromes except that the reduction of heterodisulphide CoM-S-S-CoB is performed by the membrane-bound VhoACG-HdrDE complex for former while by cytoplasmic MvhADG–HdrABC complex for the later.

ABSTRACT
Increased environmental pollution and high demand over supply for conventional fossil fuel sources have led to the exploration of other renewable energy sources. Out of the potential alternatives, methane has come up with a promising unconventional fuel source. Several technologies have been developed to establish methanation, including biomethanation, chemical methanation and gas-to-liquid technology. Several factors like process parameters, catalysts and reactors have been studied for process optimisation and making methane production commercially viable. Although methane as a greenhouse gas is a contributor to global warming, its combustion produces relatively ‘environment friendly’ gas CO₂, and H₂O. The released CO₂ is fixed into biomass by plant through photosynthesis process. Only rarely does methane combustion produces CO when combusted in an oxygen-limited condition. Thus, methane production and combustion are carbon neutral process. This chapter focuses on the application of methane as a source of alternate energy. Various technologies used, recent advances and challenges faced in methanation process are discussed.

ABSTRACT
Recent challenges for fossil fuels, associated environmental issues with disposal of organic garbage, and the rising costs for energy has encouraged researchers to search for alternate energy sources. Massive uncontrolled biogas from landfills, sludge and organic material degradation under anoxic conditions is an environmental management issue. Methane emission to atmosphere has a tremendous green house effect, but this can also be a valuable renewable energy resource. Biogas from kitchen wastes through anaerobic digestion by methanogens is such an alternate technology. Kitchen wastes are considered one of the best raw materials for biogasification. Biogas is mainly composed of 50-74% methane (CH₄), 25-28% carbon-di-oxide (CO₂), other volatile compounds including hydrogen sulphide (H₂S), water vapour and other trace gas compounds. The impurities in it, like Hydrogen sulphide (H₂S) which is corrosive, downgrade its quality. Purified biogas of higher calorific value attracts attention as a substitute fuel for additional applications. Its purification using various suitable scalable and economically-viable scrubbing technologies to enhance its quality and performance as an alternate fuel is an important step in this direction. Thus, a technology to generate and purify biogas from kitchen waste has significant prospects in the energy sector, and also as a promising enterprise to employ the youth.

ABSTRACT
Rice straw is one of the most abundant lignocellulosic agricultural wastes in India and can be used as a major resource for biogas production. The structure of rice straw is complex and is made up mainly of cellulose, hemicelluloses and lignin. The lignin physically shields the cellulose and hemicelluloses, the main components which can be converted to energy, and hinders the access of microbial enzymes to these carbohydrate moieties. To overcome this making the straw a potential resource of biogas production, effective pretreatment is necessary that can efficiently hydrolyse the straw and make it accessible to cellulolytic enzymes and methanogens. Various pretreatment methods which are employed currently in the market are discussed here. For a process to be economically feasible it is necessary that the pretreatment be efficient as well as cost effective. To gain some insights into this aspect, the techno-commercial feasibility of Biomethanation of rice straw has been discussed.

ABSTRACT
There is a quantum global growth in both number and size of dairy industries in recent times. These industries discharge thousands of liters of wastewater characterised by a heavy chemical oxygen demand (COD), biological oxygen demand (BOD), nutrients, inorganic salts, besides detergents and sanitisers each day (Kushwaha et al., 2011). High carbohydrates, fats and proteins contents in such waste streams originating from milk pose challenge to conventional municipal sewage treatment systems (Demirel et al., 2005). The easily biodegradable dairy effluent leads to precipitation of casein which is toxic and has a strong odour. Without adequate treatment, these effluents could pose serious threats to the receiving water bodies and disturb the ecosystem (Satpathy et al., 2017). For such heavily loaded organic effluents, biogasification through anaerobic digestion seems a promising strategy. This cost-effective energy generation process is also effective in environmental management compared to its other eco-friendly renewable source counterparts, such as, hydro, solar and wind (Satpathy, 2017). The Upflow Anaerobic Sludge Blanket (UASB) reactors are the most popular anaerobic systems to treat the fluctuating organic loads in dairy effluents. Anaerobic filter (AF), Upflow Anaerobic Filter (UPAF), Fixed-bed anaerobic reactor, Anaerobic Hybrid Reactor (AHR) etc. are few other systems that are demonstrably competent removing COD upto 90% and generating biogas. Successful anaerobic systems reported yielded (with upto about 85% CH₄ content) 0.32-0.34m3 CH₄/kg COD removed. Biomethanation process and reactors designs are constantly being optimised for effective treatment of the effluents while maximising the biogas production. The energy-rich gas thus obtained could further be utilised for heat and electricity at the domestic level, and also as a vehicular fuel. Residual digestate can also be used as organic manures (Angelidaki et al., 2007).

ABSTRACT
The annual accumulating organic wastes generated through milling, brewing and various other agro-industrial and agri-processing activities in India amount to about 500 MMT. Most of these contain three major structural biopolymers, viz., cellulose, hemicellulose and lignin, a large proportion of which is carbohydrate and phenolic in nature. Massive accumulations of this kind of recalcitrant biomass not only deteriorate the environment, but also generate huge amount of potentially useful materials. Biological transformation has globally become increasingly popular among researchers and academicians in treating agricultural, industrial, organic and toxic wastes these days. About 60% by weight of the original pineapple fruit in forms of peeled skin, core, crown end, etc. resulting from the pineapple processing remains unutilised being discharged as waste, causing disposal and pollution owes. Of the 10% dry matter content of such waste, about 96% is organic and 4% is inorganic. Being rich in cellulose and hemicellulose, it can be a valuable resource for numerous value-added products by bioconverting it to valuable products, including bioenergy. Biomethane is one such energy resource that can be produced by valorizing pineapple wastes by employing various bioconversion technologies. This chapter focuses on the bioconverstion technologies to produce biomethane from pineapple wastes.

ABSTRACT
Biomethanation is the process of generating energy from anaerobic digestion (AD) of organic refuse. This helps reduce dependency on fossil fuels while reducing CO₂ emission. Organically-rich kitchen refuse (KR) generated daily from the households all over the world could be a good candidate to biogasify, though the quantity and quality of biogas depends on the KR proximate. To enhance biogasification, KR is subjected to physical, chemical and/or biological pretreatment. Physical pretreatments include chopping, grinding, ultrasonication and steam explosion, whereas chemical pretreatments could be by an alkali or a base. Biological treatment involves digestion of the organic matters by individual microbe or its consortium, although it takes place naturally. For biological treatment, standardising the substrate and inoculum ratio is essential. Candidate strains must be mutually compatible to formulate a microbial consortium. Bacillius seems to be a potential candidate as it is a facultative anaerobe exhibiting multiple essential enzyme activities (viz., amylase, cellulase and lipase). Maintaining an optimal pH of 7.0 and temperature of 37°C before and during anaerobic digestion is suggested. Other important factors in digestion include organic loading rate, retention time, C/N ratio, volatile fatty acids (VFA) concentration, etc. VFA content is vital as its accumulation during acidogenesis adversely affects the AD by dropping pH values to unfavourable levels. Acetogens convert VFA to acetate, CO₂ and H₂ in acetogenesis, finally converting it to CH4 in methanogenesis. For effective methanation,

ABSTRACT
The rapid industrialisation and speedy progress towards urbanisation across the world have created the huge demand for energy moreover creating enormous quantity of wastes. The latter is in charge for polluting the environment in an exponential manner, resulting out of the harmful and poisonous compounds released by them. In recent years, there has been a pattern change from ‘waste-to-wealth’, considering the value of high organic fraction accessible in the wastes. The best-accomplished methods are that of anaerobic digestion; contribute to the production of methane (CH₄). Similar biotransformation has partial net energy harvests. Recently, fermentation industries steering their interest towards the production of value added products, viz., biohydrogen (H₂), biopolymers, ethanol, vitamins, acetic acid, enzymes, butanediols, etc. from carbon rich organic waste like cellulosic substrates have been envisioned to flourish in a multi-step process, such as the ‘Biorefinery’. A contemporary development in fermentation technology holds the key to overcome the constraints for such processes that may lead to a sustainable economy. One related technology is electrofermentation that has attracted significant attention recently owing to its capacity to boost the metabolic activities of microbes by means of extracellular electron transfer.

ABSTRACT
The global energy demand is increasing day by day. Now-a-days, the supply of global energy is highly dependent on fossil fuels (viz., crude oil, lignite, hard coal, natural gas). But, on one hand the source of fossil fuel is decreasing at a rapid rate, and on the other hand the burning of fossil fuel is also degrading the environment due to huge Greenhouse Gas (GHG) emission (FAO/CMS, 2011). Hence, there is an urgent need of an alternate clean energy source that can sustain the future development undeterred while still protecting the environment. Biomass is considered as a sustainable source of clean energy that is useful in steam, fuel and chemicals production. It can be digested in controlled environment to produce biogas. The biogas is a clean, non-polluting and smoke-free fuel (Weiland, 2010) which can be used for applications that include cooking, space cooling/refrigeration, heating, electricity generation and gaseous fuel (CNG) for vehicles (Weiland, 2003). This paper analyses possible alternatives to process and utilise biogas for energy at industrial scale as a petroleum fuels replacement.

ABSTRACT
Nisargruna biogas technology has been developed in BARC for processing different types of biodegradable waste. This technology is based on biphasic separation of aerobic and anaerobic stages. Introduction of aerobic predigester has increased the efficiency of the process in terms of enhancing the quality and quantity of methane and also reducing the retention time of the process. Process parameters of the plant were studied and found that the modified design has proved very effective in faster digestion of complex waste. The technology has been commercialised and has spread across different parts of country for processing variety of wastes including kitchen waste, municipal waste, agriculture waste, textile waste and poultry waste. Microbial population from different stages of the plant was also studied and found their ability to convert waste to biogas, containing mainly methane.

Keywords: biogas, biphasic digestion, green energy, methane, microbial fermentation, Nisargruna, waste management

ABSTRACT
The ‘steel-drum type’ biogas plant installation programme in Punjab was initiated in the early seventies, whose construction works were entrusted on the Department of Agriculture. Because of their high cost and high failure rates then, only the affluent farmers could afford to install. In the meantime, a cheaper Chinese model, also known as Janta type (dome-type replacing the drum-type) plant, became popular but was still out of the reach of ordinary farmers. With dual objectives of cost reduction and efficient biogas production to make it affordable to small farmers, a coordinated research was initiated. Detailed extension surveys were carried out in Ludhiana district which inferred that the farmers did not install the gas-holding steel drum though they purchased it. A significantly low cost Katcha-Pucca plant model of 15m3/d capacity was developed and installed in Sangrur district. Similar digester was tried for Janta model in which the inlet, outlet and dome (gas holder) were masonry works but the digester was a Katcha pit, the dome of the plant being modified to a semi-circular dome. More than 80 such plants are functional in a single village, and more than 40–50% of the plants installed in Punjab and Haryana are these PAU designs. The chapter deals in greater details on the design aspects and the extensive field works carried out to achieve these successes, along with some relevant facts and figures. The write-up is written as a commentary, rather than a typical chapter, for the benefit and interest to the readers.

ABSTRACT
It is impossible to envision a world without energy provisions and has become inevitable and an integral part of a nation’s prosperity. It has unparallel contributions towards the socioeconomic empowerment, industrial growth, and technological advancements, as also in mitigating greenhouse gas (GHG) emissions. Judicious utilisation of energy sources without infringing upon the sustainable energy demand is the need of the hour. Taking India’s huge bovine strength into account, biogas technology has the potential to transform the standard of living of rural millions. Effectively implementing the Govt. of India’s National Biogas and Organic Manure Programme (NBOMP) could save precocious demand on electricity, fertiliser and firewood while promoting self-sustainable organic farming. In this chapter, the cost and benefit analyses of biogas technology (involving biogas plants of different capacities) are discussed along with the long-term approach at the grassroots level to promote digested slurry for organic farming. The commercialisation and carbon crediting of biodigester technology is also narrated. It is necessary to chalk out a revenue model to commercialise slurry at the grassroots level to economically empower the beneficiaries, from family-type to industrial-scale biogas plants. Small-scale biofertiliser hubs at district level may be organised to collect dry slurry from the beneficiaries and process them further to make it market-ready through entities operating at the national level developing manure for agricultural practices, as also feeding materials for animal husbandry practices.

1.1    General Introduction, History, Ownership of Tangible and Intellectual Property
    
1.2    Patentable Subject Matter
    
1.3    Basic Requirements of Patentability
    
1.4    Novelty, Public Domain, Obviousness and Non Obviousness
    
1.5    Inventive Step (Non-Obviousness) and Industrial Application

2.1    Specification Drafting
    
2.2    Methods and Precautions in Drafting Patent Specification
    
2.3    Disclosure Requirements
    
2.4    Patent Claims
    
2.5    Filing of A Patent Application

3.1    Competitive and Collaborative Research
    
3.2    The Strategy to Protecting Plants : Plant Variety Protection Act
    
3.3    IPR Issues in Biotechnological Patents Haldi, Neem and Basmati Rice
    
3.4    Patent Litigation

4.1    Provisional and Complete Specifications
    
4.2    Publication
    
4.3    Request of Examination
    
4.4    PCT Application: Gateway to Foreign Patents

5.1    TRIPS
    
5.2    WTO
    
5.3    WIPO
    
5.4    Paris Convention
    
5.5    Berne Convention
    
5.6    Budapest Treaty
    
5.7    Madrid Agreement Marks (MAM)
    
5.8    The Patent Law Treaty (PLT)

6.1    Copyrights
    
6.2    Trademarks
    
6.3    Geographical Indications
    
6.4    Trade Secrets

Appendix A: Diagrammatic Presentation of Processes Involved in Obtaining an Indian Patent

1.1. INTRODUCTION

In this chapter, we will know how to classify raw data. The purpose of classifying raw data is to bring order in them so that they can be statistically analysed easily. Classification of objects or things saves our valuable time and effort. It is done in a logical manner and similar things are arranged into a group or class.

2.1. INTRODUCTION

Many biological variables do not meet the assumptions of parametric statistical tests as they are not normally distributed, the variances are not homogeneous, or both. Objects in nature commonly exhibit one of three distributions:

3.1. INTRODUCTION

In this chapter, we will study the measures of central tendency which is a numerical method to explain the data in brief. We can see examples of summarizing a large set of data in day to day life like average marks obtained by students of a class in a test, average rainfall in an area, average production in a factory, average income of persons living in a locality or working in a firm etc.

One of the most important objectives of statistical analysis is to get one single value that describes the characteristic of the entire mass of data. Such a value is called the central value or an ‘average’ or the expected value of the variable. This single value is the point of location around which individual values cluster and, therefore, called the measure of location. Since this single value has a tendency to be somewhere at the center and within the range of all values it is also known as the measure of central tendency. The purpose of computing an average value for a set of observations is to obtain a single value which is representative of all the items.

4.1. INTRODUCTION

Dispersion is the extent to which values in a distribution differ from the average of the distribution. To quantify the extent of the variation, there are certain measures namely:

1. Range
2. Quartile Deviation
3. Mean Deviation
4. Standard Deviation

Range and Quartile deviation measure the dispersion by calculating the spread within which the values lie. Mean Deviation and Standard Deviation calculate the extent to which the values differ from the average.

5.1. INTRODUCTION

Mean and standard deviation summarizes the salient features of a data set. But they do not describe adequately the full characteristics of the data. For an adequate description of the characteristics of a data set, a convenient method is to plot a frequency distribution curve. Such curve is constructed by first drawing a frequency polygon, which may then be smoothened to a frequency curve, indicating the distribution. The frequency curve or distribution of specific biological variables generally has a specific form and plays a very important role in statistical theory and practice. It is customary to describe a curve in terms of its symmetry or assymmetry and its flatness or peakedness.

The following types of distribution are commonly seen in the biological world:

6.1. INTRODUCTION

All ecologist and biology researchers have to find out: (i) how reliable are the observed results? and (ii) what is the probability of difference between observed and expected results? The question of reliability is solved by estimating the confidence interval and the second question is addressed by the application of statistical tests for significance. Both these methods help us to draw conclusions on our observations, hence, may be grouped together as ‘inferential statistics’.

Referring back to the chapter on sampling, we may recall that it is usually not possible to calculate the ‘population parameter’ directly. Instead, we have to rely on estimates. Two types of estimates can be used, a point estimate and an interval estimate. A point estimate is a single numerical value of a sample statistic that is used to estimate the corresponding population parameter. For example, the sample mean, x , is used as an estimate of the population mean, μ Point estimates are not used widely because the value of some statistic, say mean, varies from sample to sample and we can’ t conclude with any degree of certainty that a single point estimate equals the population parameter. Therefore, an interval estimate is typically favoured. An interval estimate is a range of values within which the population parameter is likely to lie.